Alex is the CEO and Founder at Scale AI. He was inspired to solve ML infrastructure problems and accelerate the development of AI through his work at Quora, where he worked as a technical lead. Alex worked as an algorithm developer at Hudson River Trading and as a software engineer at Addepar. He attended, and dropped out from, MIT, studying Artificial Intelligence.

image.jpg

Alexandr Wang

CEO and Founder at Scale AI

Scale AI is pleased to announce the launch of Sensor Fusion Segmentation - Scale’s endpoint for point cloud segmentation.<iframe class="ql-video" frameborder="0" allowfullscreen="true" src="https://lidar-now.scale.com/examples/semseg" height="100%" width="100%"></iframe> Sensor Fusion Viewer rendering a scene from nuScenes Released at CVPR 2019, Sensor Fusion Segmentation provides the highest precision for annotating complex objects that cannot be easily described with LiDAR cuboid labeling. Examples include vegetation, smoke/exhaust, splashes/puddles, rain and reflections.<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/34fe7aaa-a1a6-4737-823a-ffe19dbe8b00/public" alt="fog seen through LiDAR">Fog<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/bbbb9168-544a-4866-b096-9bb07cf87c00/public" alt="fog seen through LiDAR">Smoke/Exhaust<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/6cd0ad4c-0d0a-48c6-65f3-e18ac64a1700/public" alt="vegetation seen through LiDAR">Vegetation<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/ae620a78-79bd-4c4c-2ade-0e7de9ddf500/public" alt="Trucks with open backs seen through LiDAR">Trucks with open backs<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/9cb13a70-42a2-4559-1c44-a5b34bebbc00/public" alt="reflections seen through LiDAR">Reflections<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/dff8770c-e1cc-4e77-8d73-d35007f3f200/public" alt="Splashes &amp; Puddles seen through LiDAR">Splashes &amp; Puddles Semantic scene understanding is important for a variety of applications, particularly autonomous driving. Rigorously tested by a handful of Scale’s customers during a private beta, Sensor Fusion Segmentation annotation provides fine-grained understanding of surfaces and objects in a 3D point cloud. Take a look below, for how we helped the Toyota Research Institute (TRI) accelerate their research projects by giving them greater flexibility and the ability to amend workflows.<iframe class="ql-video" frameborder="0" allowfullscreen="true" src="https://www.youtube.com/embed/2XURB1nE11w"></iframe> Since starting its work with Scale, TRI has been able to support four large annotation pipelines without significantly increasing the size of their team.<a href="https://scale.ai/sensor-fusion-segmentation" rel="noopener noreferrer" target="_blank">Learn More</a>

Introducing Point Cloud Segmentation

We’ve had an incredible group of people join Scale—and we’re still <a href="https://scale.com/about#jobs" rel="noopener noreferrer" target="_blank">hiring</a>! We wanted to take a moment to follow up on <a href="https://scale.com/blog/scale-engineering-team" rel="noopener noreferrer" target="_blank">this post</a> and call out the latest team members that have joined the Engineering, Product and Design teams at Scale: <ul><li> </li><li>Catherine Wu / Catherine is a full-stack engineer at Scale working primarily on our infrastructure team. She has previously worked as an engineer at Google and product manager at Yelp. She studied computer science at Princeton University, where she also competed on the synchronized skating team.</li><li> </li><li> </li><li>Eugene Fratkin / Prior to Scale, Eugene led R&amp;D for the Cloud division at Cloudera, responsible for the development of managed distributed data processing services in AWS/Azure. Prior to Cloudera, Eugene was a co-founder and VP of R&amp;D at Lifecode – a Sequoia Capital-backed genomic cancer diagnostic start-up. Prior to Lifecode, he was one of the founding members of Greenplum’s machine learning team, which started the MADlib Apache project. Eugene holds a PhD in Machine Learning from Stanford. Eugene also served as an assault team section leader in the United States Marine Corps for missions such as the anti-terrorism quick reaction force within the continental US, and the initial phase of Operation Iraqi Freedom.</li><li> </li><li> </li><li>Gabriel Andretta / Gabriel previously worked at <a href="https://auth0.com/" rel="noopener noreferrer" target="_blank">Auth0</a> with Ricky, Martín and Ramiro, where he rebuilt <a href="https://github.com/auth0/lock" rel="noopener noreferrer" target="_blank">Lock</a> from scratch and was the lead developer of the browser SDKs. He was a contributor to the sinatra web framework, a coach in the first edition of <a href="https://twitter.com/ClojureBridgeBA" rel="noopener noreferrer" target="_blank">Clojure Bridge Buenos Aires edition</a>, and built a <a href="https://github.com/gnandretta/literate-2048" rel="noopener noreferrer" target="_blank">2048</a> clone with almost no mutable state.</li><li> </li><li> </li><li>Steven Salka / <a href="http://ssalka.io/" rel="noopener noreferrer" target="_blank">Steven</a> joined the engineering team at Scale as a product-focused full-stack engineer, working largely on internal applications and the frontend for Scale’s LiDAR tool. Previously, he was the first engineering hire at <a href="https://podible.co/" rel="noopener noreferrer" target="_blank">Podible</a>, and before that built out a UI framework for text-based annotations at <a href="https://x.ai/" rel="noopener noreferrer" target="_blank">x.ai</a>. Steven studied Applied Mathematics at UC Berkeley.</li><li> </li><li> </li><li>Ramiro Silveyra d'Avila / Prior to Scale, Ramiro was a software engineer at <a href="https://auth0.com/" rel="noopener noreferrer" target="_blank">Auth0</a> working on the marketing and engineering teams. He loves open source software, especially web tooling. He has contributed to yarn, babel, stylelint, eslint plugins and webpack loaders.</li><li> </li><li> </li><li>Michael Woo / Michael is a supply product manager at Scale. After graduating from Harvard in Computer Science, he joined McKinsey &amp; Company as a business analyst. Michael is a former captain of the Harvard Fencing Team and National Junior Team Member for the US National Fencing Team.</li><li> </li><li> </li><li>Sara Xiang / Sara is a supply product manager at Scale. She previously worked at Twitter on the Moments team, and at Facebook on the Oculus Developer Technologies and Oculus TV teams. She studied computer science at Carnegie Mellon University.</li><li> </li><li> </li><li>Pato Perez / Pato is a full-stack engineer at Scale working primarily on our LiDAR annotation tool. He previously worked as an Android and iOS engineer at other startups. He was the winner of multiple software engineering competitions, including Microsoft Imagine Cup @ Australia.</li><li> </li><li> </li><li>Shivaal Roy / Shivaal graduated from Stanford with a B.S. and M.S. in computer science. He founded Stanford's health hackathon, <a href="http://healthplusplus.stanford.edu/" rel="noopener noreferrer" target="_blank">Health++</a>, and was president of <a href="http://shift.stanford.edu/" rel="noopener noreferrer" target="_blank">SHIFT</a>, Stanford’s health-tech student organization. Prior to Scale, he worked at Google on the Photos Machine Intelligence team, and at Oscar Health.</li><li> </li><li> </li><li>Mauro Giamberardino / Mauro is a senior full-stack engineer who previously worked for companies like Globant and Spark Digital. He studied Systems Engineering at Buenos Aires University.</li><li> </li><li> </li><li>Martin Cabral / Martin joins Scale as a software engineer with ten years of experience in different positions and companies. His last adventure was as a product manager at <a href="https://www.auth0.com/" rel="noopener noreferrer" target="_blank">Auth0</a> where he learned to work very closely with customers and understand how they approach products. When he is not working, you can find him gaming (Dota2, CS, Fortnite et al.) or playing real life tennis.</li><li> </li><li> </li><li>Nathan Hayflick / Nathan is a frontend engineer at Scale. He joins from Uber, where he worked for 3 years on the Safety Engineering and Develeper Platform teams, and worked at Rockbot before that. He studied Literature at Stanford University.</li><li> </li><li> </li><li>Jeff Hilnbrand / Jeff is the Design Manager at Scale. He was most recently a Senior Product Designer at Facebook on the Groups and Payments teams. Prior to that, Jeff founded a design agency called Minimill and worked on design teams at Autodesk and Venmo. Jeff studied product design and engineering at the University of Maryland.</li><li> </li><li> </li><li>Andrew Wang / Andrew was a tech lead at Cloudera working on HDFS and cloud-based machine learning platforms for 6 years. He’s one of the biggest contributors to the open-source Apache Hadoop project, served as the release manager for Apache Hadoop 3.0, and is an Apache member. Before that, he was a PhD student in distributed systems at UC Berkeley and studied computer science at the University of Virginia.</li><li> </li><li> </li><li>Craig Weiss / Craig joins Scale with experience from Lyft, Snapchat, Facebook, and Google. Craig frequently competed in and won several awards at regional hackathons and competitive programming competitions (including the ACM-ICPC). Craig studied computer science at the University of Maryland.</li><li> </li><li> </li><li>Aatish Nayak / Aatish is a supply product manager at Scale, with previous experience at Shield AI, Uber, MongoDB, and Skurt. At ShieldAI he worked on life-saving autonomous robots for special operations and law enforcement use cases. Aatish studied computer engineering at Carnegie Mellon University and led Autolab, a startup for auto-grading CS homework assignments.</li><li> </li><li> </li><li>Ty Wilkins / Ty has worked closely with Scale from the beginning to establish Scale’s branding, icon system, and illustration style. Previously, Ty was an early employee at <a href="https://dribbble.com/shots/2994377-Robinhood-Gold" rel="noopener noreferrer" target="_blank">Robinhood</a> where he created product UI, branding, illustration, and marketing. He has extensive experience in branding, working with clients including Google, <a href="https://dribbble.com/shots/2347496-Facebook" rel="noopener noreferrer" target="_blank">Facebook</a>, and <a href="https://dribbble.com/shots/195804-Target-Education" rel="noopener noreferrer" target="_blank">Target</a>. Ty has also branded numerous startups including <a href="https://auth0.com/" rel="noopener noreferrer" target="_blank">Auth0</a>, <a href="https://www.useloom.com/" rel="noopener noreferrer" target="_blank">Loom</a>, and <a href="https://dribbble.com/shots/314295-Orchestra" rel="noopener noreferrer" target="_blank">Orchestra</a> in addition to many open source projects including <a href="https://dribbble.com/shots/4738771-Celery" rel="noopener noreferrer" target="_blank">Celery</a> and <a href="https://dribbble.com/shots/2153014-JWT" rel="noopener noreferrer" target="_blank">JWT</a>. His editorial illustration work has been featured in publications including <a href="https://dribbble.com/shots/192949-Monocle" rel="noopener noreferrer" target="_blank">Monocle</a>, <a href="https://dribbble.com/shots/1215603-Bloomberg-Markets" rel="noopener noreferrer" target="_blank">Bloomberg Markets</a>, <a href="https://dribbble.com/shots/2083018-ESPN" rel="noopener noreferrer" target="_blank">ESPN Magazine</a>, and <a href="https://dribbble.com/shots/766236-Wired-UK" rel="noopener noreferrer" target="_blank">Wired UK</a>. Ty holds a B.F.A. in Graphic Design from Auburn University.</li><li> </li><li> </li><li>Facundo Pepe / Facundo is a UI Developer in charge of the marketing landing pages at Scale. He previously worked at Infobae, the most popular news media in Argentina and at <a href="https://aerolab.co/" rel="noopener noreferrer" target="_blank">Aerolab</a>, a digital agency where he and his team won a <a href="https://www.a24.com/actualidad/a24com-gano-un-martin-fierro-digital-por-la-aplicacion-juga-24-12112018_H1xcRshkN" rel="noopener noreferrer" target="_blank">Martín Fierro Digital award for Best New Application</a> with a betting game for the World Cup. Better known as Turbo because he makes things fast, Facundo loves to deliver pixel perfect projects while working on user interactions that look cool and also perform well.</li><li> </li></ul>

Scale's Engineering, Product and Design Teams - Meet Our Team

Scale Engineering, Product and Design Teams

Scale AI is pleased to announce the full release of <a href="https://www.nuscenes.org/" rel="noopener noreferrer" target="_blank">nuScenes by Aptiv</a> —a large-scale open source dataset for autonomous driving. Scale AI was involved as Aptiv's data labeling partner on this initiative. On March 28, 2019, Aptiv announced the full dataset release of nuScenes, an initiative intended to broadly support research into computer vision and autonomous driving by AV innovators and academic researchers to further advance the mobility industry. One key bottleneck in the advancement of deep learning is access to labeled data. Most machine learning algorithms learn from large amounts of examples to understand the world. These incredible algorithms are powered behind the scenes by billions of hand-labeled examples of what humans can easily understand. While we like to believe as computer scientists that it is all about the algorithms, labeled data is incredibly impactful to progress. The current age of deep learning was ignited by the launch of the ImageNet dataset by Fei-Fei Li's lab at Stanford in 2009. As Fei-Fei said once in an interview: <blockquote>Our vision was that Big Data would change the way machine learning works. Data drives learning.</blockquote><blockquote> </blockquote>The release of the full 1000 scenes means nuScenes now includes 1.4M camera images, 390k LiDAR sweeps, 1.4M RADAR sweeps and 1.4M object bounding boxes in 40k keyframes. nuScenes is the largest multi-sensor dataset for autonomous vehicle data with LIDAR, RADAR, camera, IMU, and GPS, and builds upon great work in the field by others such as the KITTI dataset. Collected in Boston and Singapore, nuScenes data contains 7x more object annotations than KITTI and will enable researchers, students and developers to study challenging urban driving situations. <iframe class="ql-video" frameborder="0" allowfullscreen="true" src="https://lidar-now.scale.com/examples/demo" height="100%" width="100%"></iframe> Sensor Fusion Viewer From conception, we partnered with Aptiv to produce the nuScenes dataset. By leveraging Aptiv's autonomy stack and <a href="https://scale.com/sensor-fusion-annotation" rel="noopener noreferrer" target="_blank">our sensor fusion and LIDAR annotation products</a>, nuScenes sets a new standard for quality in public datasets, along with a web-based visualizer for LIDAR and camera data for <a href="https://www.nuscenes.org/explore/scene-0120/0" rel="noopener noreferrer" target="_blank">exploring the dataset</a>. It is crucial to machine learning development to be able to visualize data to develop intuitions about the problems at hand. We, <a href="https://eng.uber.com/avs-autonomous-vehicle-visualization/" rel="noopener noreferrer" target="_blank">like</a> <a href="https://medium.com/cruise/introducing-worldview-749aaf98112d" rel="noopener noreferrer" target="_blank">others</a>, noticed a gap where previous options like <a href="http://wiki.ros.org/rviz" rel="noopener noreferrer" target="_blank">rviz</a> were not built for a web-based world. Our LiDAR visualizer allows point cloud data to be easily embedded into any webpage and shared. We've also built it to be developer-friendly and usable for a broad range of use cases. We hope that the nuScenes dataset will become the new standard in the industry and allow researchers to develop safer autonomous driving technology. There is a large gap today in the machine learning community—the larger organizations have significantly larger datasets and compute budgets than smaller organizations or universities. Our hope is to bridge that gap and democratize access to state-of-the-art data in autonomous vehicles. With the launch of nuScenes, we hope to do for the autonomous vehicle community what ImageNet did for computer vision.

nuScenes by Aptiv ImageNet for Autonomous Vehicles

Scale is committed to accelerating the development of AI applications and serving customers with high-speed, accurate, and affordable data annotation. As part of this mission, Scale and Ouster are pleased to announce a data integration partnership that makes it significantly easier and faster for Ouster customers to send their LiDAR data to the Scale platform for annotation. The two companies have worked together for months to establish a common data format and labeling process that combines the image-like output from Ouster’s lidar sensors and the industry-leading capabilities of Scale’s Sensor Fusion annotation (annotation for LiDAR and RADAR) and Semantic Segmentation annotation solutions to save perception teams time and work. The new approach removes some of the most difficult and time-consuming aspects of data labeling for companies working on everything from autonomous vehicles to industrial robotics and drones. The partnership has a number of specific advantages for customers, including: <ul><li>Streamlined data transfer - A standardized, efficient data</li><li> format reduces the amount of work customers must perform prior to sending</li><li> data for annotation.</li><li> </li><li>No sensor fusion required - Ouster’s lidar sensor outputs</li><li> 2D camera-like imagery in addition to 3D data, reducing the time spent on</li><li> calibrating cameras.</li><li> </li><li>Automatic 2D Mask Generation - Customers can now send a</li><li> single image with 2 API calls to Scale and receive both 2D instance and</li><li> semantic segmentation masks and 3D bounding boxes.</li><li> </li><li> </li></ul><img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/8fdb9340-ff9b-47c0-8ac0-b9811d03c400/public" alt="Point cloud computed only from depth information encoded in image pixels."> Point cloud computed only from depth information encoded in image pixels. Ouster and Scale are leading the industry with this first-of-a-kind integration. In the coming months, Scale intends to form similar data integration partnerships with a broad ecosystem of sensor manufacturers to continue to improve customer experience for LiDAR and RADAR annotation.<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/ec025881-9915-4b23-e38c-b5d13ac21300/public" alt="Full 360-degree camera view helps taskers locate and annotate objects."> Full 360-degree camera view helps taskers locate and annotate objects. These partnerships will reduce the amount of work customers have to put in to send their data to Scale.AI, to develop a more seamless data pipeline.<img src="https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/91d02897-d750-45b9-96e3-8867cf5e8700/public" alt="Accurate projections of cuboids from 3D to 2D.">Accurate projections of cuboids from 3D to 2D.

Scale is committed to accelerating the development of AI applications and serving customers with high-speed, accurate, and affordable data annotation.

Announcing the Scale and Ouster Data Integration Partnership - Scale

Announcing the Scale and Ouster Data Integration Partnership

We have exciting news to share today! Scale raised $18M in Series B funding led by Index Ventures with Accel, Y Combinator, Drew Houston, and Justin Kan joining. Mike Volpi of Index Ventures will be joining our board. Scale has come a long way from the dorms at MIT, and we still have a long way to go. Our mission at Scale is to accelerate the development of AI applications. We are strong believers in the transformational impact of AI and deep learning across every piece of the global economy—from manufacturing and agriculture to autonomous vehicles and security. Deep learning is eating the world in computer vision—models that outperform humans have become standard. It is an important and exciting transition for us—the future will happen very quickly. The bottleneck that becomes apparent to every practitioner is labeled data. The most significant bit to the performance of your AI software is no longer the code you write, but rather the data you are training on. In fact, your code becomes a servant to your ever-growing data corpus—you build hacks to maintain the key invariant of data integrity. The way you improve your software is not to write more code, but to label more data. So when we founded Scale, we devoted ourselves to solving the problem of data labeling. We saw parallels between building web software before cloud computing and AI development today. Before cloud computing, a significant hurdle to building on the internet was building and operating large server farms to power your software. Ultimately, that activation energy prevented many applications from being built. In the same way, a significant hurdle to building machine learning is building the capability to label large datasets. We’re aiming to accomplish exactly what cloud computing did—offer reliable, cost-effective, scalable infrastructure to make it easier to build incredible applications. Scale turned 2 years old last month, and we are well on our way. In autonomous driving, one of the most prominent applications of deep learning today, Scale has become the industry standard for labeling data. We’ve partnered with many industry leaders such as GM Cruise, Lyft, Zoox, and nuTonomy. We’ve labeled more than 200,000 miles of self-driving data (about the distance to the moon). Scale is also used by tech giants like Airbnb and Pinterest, and Fortune 500 companies like P&amp;G and Liberty Mutual. We are proud that our partners have made Scale an integral and trusted part of their pioneering work. Our Series B funding reflects the next stage in our journey, and we are not slowing down. We have a lot left to build, from supporting new applications to creating the best experience around training on labeled datasets. We strive to significantly accelerate the development of AI in every application that makes the future happen faster. To that end, one of our primary goals with the new funding will be growing <a href="https://scale.com/blog/scale-engineering-team" rel="noopener noreferrer" target="_blank">our team</a>. If you want to make a difference in the world of AI, come <a href="https://scale.com/about#jobs" rel="noopener noreferrer" target="_blank">join us</a> on our mission. AI is just taking off. We’re excited to be fueling it.

Scale raised $18M in Series B funding led by Index Ventures with Accel, Y Combinator, Drew Houston, and Justin Kan joining.

Scale raised $18M in Series B with Index Ventures and Accel

Announcing Our Series B with Index Ventures and Accel

We’ve had an incredible group of people join Scale—and we’re still <a href="https://scale.ai/about#jobs" rel="noopener noreferrer" target="_blank">hiring</a>! We wanted to take a moment to call out the amazing engineering team at Scale: <ul><li>Ricky Rauch / <a href="https://twitter.com/rickyrauch" rel="noopener noreferrer" target="_blank">Ricky</a> was the first designer and later Head of Design at <a href="https://auth0.com/" rel="noopener noreferrer" target="_blank">Auth0</a>. He has designed many developer-focused products including <a href="https://github.com/gitlabhq/gitlabhq" rel="noopener noreferrer" target="_blank">GitLab</a>, <a href="https://jwt.io/" rel="noopener noreferrer" target="_blank">JWT.IO</a>, and <a href="http://www.passportjs.org/" rel="noopener noreferrer" target="_blank">Passport.js</a>.</li></ul> <ul><li>Nate Herman / Nate most recently worked on infrastructure engineering at Dropbox. In the past he built an <a href="https://www.wired.com/2012/09/gameception-doom-iphone/" rel="noopener noreferrer" target="_blank">iOS port of Doom</a> and its associated mods, as well as a popular <a href="https://venturebeat.com/2016/08/18/new-pokemon-go-bot-launches-as-niantic-starts-permanently-banning-cheaters/" rel="noopener noreferrer" target="_blank">Pokemon Go</a> automator. He studied computer science at Harvard with a research focus on <a href="http://www.read.seas.harvard.edu/~kohler/pubs/herman16type-aware.pdf" rel="noopener noreferrer" target="_blank">concurrent computing</a>.</li><li> </li><li> </li><li>Steven Hao / Steven graduated from MIT and previously had stints at Jane Street, DE Shaw, and Quora. He received a gold medal at <a href="http://stats.ioinformatics.org/people/3113" rel="noopener noreferrer" target="_blank">IOI</a> and top honors at programming competitions including Codeforces and ACM-ICPC. His personal projects include the collaborative crossword site <a href="https://downforacross.com/" rel="noopener noreferrer" target="_blank">Down For A Cross</a> and a web implementation of <a href="http://set.stevenhao.com/" rel="noopener noreferrer" target="_blank">Set</a>.</li><li> </li><li> </li><li>Leigh Marie Braswell / Leigh Marie has previously worked at Google, Jane Street, and Blend with roles in software engineering, machine learning research, and product management. She was recognized as one of the top 10 competitive female mathematicians in the United States by the Math Prize for Girls Competition. She studied computer science at MIT.</li><li> </li><li> </li><li>Calvin Huang / <a href="https://calvinhuang.me/" rel="noopener noreferrer" target="_blank">Calvin</a> graduated from MIT with a research focus in <a href="https://dreal.github.io/" rel="noopener noreferrer" target="_blank">theorem solving</a> and natural language processing, and with experience at Quora and Google prior to joining Scale. He received two gold medals at <a href="http://ipho2013.dk/ipho2013-results-gold.htm" rel="noopener noreferrer" target="_blank">IPhO</a> as well as the award for Best Physics Experiment, and was a USAJMO winner. He has been known to build robots.</li><li> </li><li> </li><li>Guido Maliandi / Guido previously founded a virtual reality startup for collaborative environments in VR using Unity and three.js. He has led engineering teams at startups for the past nine years and was a major contributor to Microsoft’s <a href="https://prismlibrary.github.io/docs/" rel="noopener noreferrer" target="_blank">Prism library</a>.</li><li> </li><li> </li><li>Zhichun Li / Zhichun has previously worked at Lightspeed China Partners, Facebook, Microsoft and Airbnb with roles in investment and software engineering. She is a producer of <a href="https://mp.weixin.qq.com/s/Kph9YZ1Nvwf4pCt9inT0-A" rel="noopener noreferrer" target="_blank">VC Pulse</a>, a podcast spotlighting venture capitalists in China. Zhichun was the youngest ever admit to the Yale MBA program, and studied computer science at CMU. She has notably been featured on Eminem’s Snapchat story.</li><li> </li><li> </li><li>Rilka / <a href="http://rilka.li/" rel="noopener noreferrer" target="_blank">Rilka</a> previously led design and frontend engineering at a startup and worked as a full-stack engineer at Dropbox. Along with Nate she built a popular <a href="https://venturebeat.com/2016/08/18/new-pokemon-go-bot-launches-as-niantic-starts-permanently-banning-cheaters/" rel="noopener noreferrer" target="_blank">Pokémon Go</a> automator; other past projects include desktop Threes, Facebook Unfriend Finder, and the Quantified Piñata. She studied computer science at Harvard and (briefly) architecture at Parsons.</li><li> </li><li> </li><li>Sam Clearman / Sam received a PhD in mathematics studying <a href="https://vimeo.com/123640110" rel="noopener noreferrer" target="_blank">combinatorial interpretations of quantum immanants</a>. He was previously an engineer at Google working on Google Play Services. He also built a variant of Tetris called <a href="http://www.samclearman.com/2etris/" rel="noopener noreferrer" target="_blank">2etris</a>.</li><li> </li><li> </li><li>Chiao-Lun Cheng / Chiao received a PhD in computational chemistry from MIT, and has a diverse background in management consulting, quantitative trading, backend engineering, and data science. He has built models for supermarket revenue prediction, bookmaking sports bets, and PCB defect detection, and is intimately familiar with bugs in the Interactive Brokers API.</li><li> </li><li> </li><li>Akshat Bubna / Akshat was the first competitor from India to receive a gold medal at the <a href="http://stats.ioinformatics.org/people/3063" rel="noopener noreferrer" target="_blank">IOI</a>. He previously has worked at DE Shaw and database and fintech startups, and studied computer science and mathematics at MIT.</li><li> </li></ul><h2>Contractors</h2> <ul><li> </li><li>Albert Sheu / Albert was the second engineer at Quora and built a Python framework to render webpages on multiple cores, which sped up Quora by <a href="https://blog.quora.com/Improving-Site-Speed" rel="noopener noreferrer" target="_blank">up to 10x</a>. He also was the Head of Engineering at Patreon and an early engineer at both Facebook and Twitter.</li><li> </li></ul>

Scale Engineering Team - Meet our Team - Scale

Scale Engineering Team++

We recently worked with the team at Cloudinary to help build and evaluate a better image compression and quality measurements. The results of Cloudinary’s work on Scale were very insightful and we wanted to share it broadly to demonstrate how more companies can leverage human judgments to build high quality features. Without further ado, <a href="https://cloudinary.com/blog/author/tal_lev_ami" rel="noopener noreferrer" target="_blank">Tal Lev Ami</a>, CTO of Cloudinary, wanted to write about how they used Scale: <blockquote> Here at Cloudinary, we provide a cloud-based tool that enables our users to</blockquote><blockquote> compress images and video for their websites and apps. Our goal is to</blockquote><blockquote> preserve the visual integrity of the content, but deliver the smallest file</blockquote><blockquote> size to any device or browser to ultimately optimize website performance and</blockquote><blockquote> end user satisfaction.</blockquote> <blockquote> One of the hallmarks of the Cloudinary solution is the ability to automate</blockquote><blockquote> many functions of image compression, so that developers don’t have to spend</blockquote><blockquote> time tweaking each photo and making multiple copies of different sizes and</blockquote><blockquote> resolutions to fit every possible scenario. Compression algorithms can be</blockquote><blockquote> tricky because they’re trying to make changes that have the smallest visual</blockquote><blockquote> impact, but different images can react differently to compression.</blockquote> <blockquote> As we were developing the algorithm for our “q_auto” capabilities — which</blockquote><blockquote> strikes a balance between visual quality and file size — we needed to test</blockquote><blockquote> it to understand how the resulting images compared to the human eye. Enter</blockquote><blockquote> Scale.</blockquote> <blockquote> Many image compression formats — like JPEG 2000 and JPEG XR — have been</blockquote><blockquote> tweaked to score well on particular metrics, such as peak signal-to-noise</blockquote><blockquote> ratio (PSNR). But these don’t always correlate with human perception on</blockquote><blockquote> image quality.</blockquote> <blockquote> We leveraged Scale to compare pairs of images and give us perspective on</blockquote><blockquote> which image was liked most by humans. With Scale, we did a variety of tests,</blockquote><blockquote> comparing several formats, including WebP, JPEG 2000, JPEG XR (lossy) Lepton</blockquote><blockquote> (MozJPEG, recompressed with Lepton), FLIF, BPG, Daala, and PNG8</blockquote><blockquote> (pngquant+optipng). We also were able to get input on the difference between</blockquote><blockquote> the uncompressed original image vs. a compressed version.</blockquote> <blockquote> Scale enabled us to create A/B comparisons that were viewed by human</blockquote><blockquote> observers. We submitted over 4,000 image comparisons to Scale, sending at</blockquote><blockquote> least four independent Scale requests for each pair of image. This resulted</blockquote><blockquote> in at least eight actual human comparisons for each pair of images. The</blockquote><blockquote> outcome of these comparisons were evaluated beside other perceptual metrics</blockquote><blockquote> such as PSNR, Google’s Butteraugli, DSSIM (Structural (Dis)Similarity) and a</blockquote><blockquote> new metric Cloudinary developed called</blockquote><blockquote> <a href="https://cloudinary.com/blog/detecting_the_psychovisual_impact_of_compression_related_artifacts_using_ssimulacra" rel="noopener noreferrer" target="_blank">SSIMULACRA (Structural SIMilarity Unveiling Local And Compression Related</a></blockquote><blockquote><a href="https://cloudinary.com/blog/detecting_the_psychovisual_impact_of_compression_related_artifacts_using_ssimulacra" rel="noopener noreferrer" target="_blank"> Artifacts)</a>.</blockquote> <blockquote> The results showed that overall, PSNR is “correct” in only 67 percent of the</blockquote><blockquote> cases. Butteraugli gets it right in 80 percent of the cases, and DSSIM in 82</blockquote><blockquote> percent of the cases. Our new metric, SSIMULACRA, agrees with human</blockquote><blockquote> judgments in 87 percent of the cases. Looking just at the high-confidence</blockquote><blockquote> human judgments, we found about 78 percent agreement for PSNR, 91 percent</blockquote><blockquote> for both Butteraugli and DSSIM, and almost 98 percent agreement for</blockquote><blockquote> SSIMULACRA. You can</blockquote><blockquote> <a href="https://cloudinary.com/blog/detecting_the_psychovisual_impact_of_compression_related_artifacts_using_ssimulacra" rel="noopener noreferrer" target="_blank">read more about SSIMULACRA and these results on the Cloudinary blog</a>. Or if you want to give it a try:</blockquote><blockquote> <a href="https://github.com/cloudinary/ssimulacra" rel="noopener noreferrer" target="_blank">SSIMULACRA is free and open-source software!</a></blockquote> <blockquote> The results of Scale comparisons gave us useful data points to validate our</blockquote><blockquote> metrics and provided more insights into the compression benchmarks we are</blockquote><blockquote> running and the comparison of various image formats. And from these insights</blockquote><blockquote> we were able to improve our visual perception metrics and fine-tune our</blockquote><blockquote> “q_auto” functionality so we know how aggressively we can compress images.</blockquote> <blockquote> Through this process we were impressed not only by the useful data points</blockquote><blockquote> derived from the Scale, but also the great support we got from the company</blockquote><blockquote> and the product’s ease-of-use, all which came at a reasonable price.</blockquote>

We recently worked with the team at Cloudinary to help build and evaluate a better image compression and quality measurements.

Image Compression: Cloudinary Relies on Scale Technology

Guest Post: Cloudinary Uses Scale to Focus on Image Compression

We are excited to&nbsp;<a href="https://www.wsj.com/articles/scale-helps-engineers-outsource-microtasks-1495452603" rel="noopener noreferrer" target="_blank">announce</a>&nbsp;that we have raised a $4.5 million Series A round of funding led by Accel. Along with this funding, Accel’s Daniel Levine has joined Scale’s board. The funding will be used to invest in our rapid growth, expand our offerings, and grow our team. When we started Scale ten months ago, our goal was to build the simplest API for training data. Since then we have released six APIs (<a href="https://scale.com/image-annotation" rel="noopener noreferrer" target="_blank">Image Annotation</a>,&nbsp;<a href="https://scale.com/ocr-transcription" rel="noopener noreferrer" target="_blank">OCR Transcription</a>,&nbsp;<a href="https://scale.com/categorization" rel="noopener noreferrer" target="_blank">Categorization</a>,&nbsp;<a href="https://scale.com/comparison" rel="noopener noreferrer" target="_blank">Comparison</a>, and&nbsp;<a href="https://scale.com/data-collection" rel="noopener noreferrer" target="_blank">Data Collection</a>), and we process millions of API requests each month. This success has confirmed our conviction that there is no better time than now to build Scale. We are currently at an inflection point in the age of artificial intelligence (AI). Just like software before it, AI will have an enormous effect on productivity and enable incredible new technologies. Scale works with many companies and technologies at the forefront of this shift, including self-driving cars, intelligent assistants, and voice analytics. Our customers agree that integrating AI with accurate human intelligence is crucial to building reliable AI technology. As a result, we believe Scale will be a foundation for the next wave of development in AI. This financing will allow us to grow our engineering, operations, support, marketing, and sales teams to continue on our journey to make our customers more productive and efficient. We’re committed to continuously improving our costs, speed, and quality and to providing a quality customer experience. We’d also like to thank our colleagues, customers, partners, and investors for supporting on this journey to build APIs for human intelligence. We look forward to continuing to support companies and developers in creating innovative software in the age of AI. This is just the beginning. We are actively hiring exceptional engineers, designers, AI researchers, and more. If you are interested in joining Scale, check out our&nbsp;<a href="https://scale.com/about#jobs" rel="noopener noreferrer" target="_blank">careers page</a>.

Announcing Our Series A With Accel

One of the largest automobile companies in the world uses Scale to help build self-driving cars. Self-driving cars (SDCs) are one of the most exciting technologies being developed today. If they become a reality, the benefits that come will significantly impact our lives in several ways:<ul><li>Lives will be saved</li><li>Transportation speeds go up → Time is saved.</li><li>People will be freed up to be productive or enjoy entertainment on the road</li></ul>One of the big challenges for SDCs is&nbsp;extremely&nbsp;high-stakes computer vision. SDCs need to quickly recognize various road conditions, animals, people, other cars, etc. They need to understand how these things are behaving and how they’re likely to behave in the near future. Computer vision, by way of deep learning, has come a long way recently, but it needs to keep improving to ensure SDCs are safe and reliable. SDC companies are using Scale to tag objects by passing images through Scale’s&nbsp;<a href="http://scale.com/image-recognition" rel="noopener noreferrer" target="_blank">Image Recognition API</a>. Scalers label cars, people, signs, traffic lights, etc. to help train their models. An example call would look something like this: <pre class="ql-syntax" spellcheck="false">import scaleapi
client = scaleapi.ScaleClient('SCALE_API_KEY')
client.create_annotation_task(
 instruction='Draw a tight box around each **car** and **pedestrian**.'
 attachment_type='image',
 attachment='https://imagedelivery.net/wLbZE4_NzVVdgHc15St55g/e2bea5ad-8ea7-4726-6f9e-9e99264c0600/public',
 objects_to_annotate=['car', 'pedestrian'],
 with_labels=True,
 callback_url='http://www.example.com/callback'
)
</pre>At Scale, we deeply believe in investing in our customer experience. It’s great to have customers that push us, particularly in ways that benefit all customers. We worked closely with the automobile company to make sure our quality was up to their standards, while maintaining a reasonable cost. Some examples of specific things we did to deliver for the customer:<ul><li>Our Scalers take online tests to determine their skill level at a specific task. We improved the Image Recognition test to ensure that Scalers were bounding objects within a pixel of accuracy on each image. Not every customer needs that accuracy, but it’s nice to have nonetheless!</li><li>We invested in the software tool our Scalers use to annotate images to improve their UX. Improving the performance and layout of the tool made a big difference. We took the cost savings and passed it through to our customers.</li><li>We noticed our Scalers were still taking a bit longer to get through the images than we had expected. When we investigated, we realized they were spending a lot of time waiting for images to load. We spun up pre-fetching of the next image to speed things up. And again, we were able to take the cost savings and pass them through to our customers.</li></ul>There are countless more things we did to improve the Scale experience that our now available to all of our customers. And we promise to keep improving it. At Scale, we want to make our customers more productive and efficient. AI and self-driving cars are an awesome application of Scale towards that goal. If you need help with training your computer vision for self-driving cars,&nbsp;<a href="https://chat.scale.ai/" rel="noopener noreferrer" target="_blank">join our Slack</a>&nbsp;and ask. We’re confident we can deliver the cost, quality and Scale (pun intended) you need!