Scale’s mission is to accelerate the development of AI by democratizing access to intelligent data. Ultimately, we're looking to build the AWS for AI, and data labeling is our first product. Our suite of managed data labeling services combine manual labeling with best in class tools and AI-driven checks to yield stunningly accurate training data. Scale is committed to continual innovation in combining humans with AI to prepare intelligent data, passing on these improvements to our customers and powering a growing future of AI applications. Our customers include Alphabet (Google), Zoox, Lyft, Pinterest, Airbnb, nuTonomy, and many more, and we've become an industry standard for the self-driving car market.
For more information about our team, check out our blog: https://www.scaleapi.com/blog
We're backed by top investors (Accel, Index, Founders Fund), have breakout traction, and are rapidly growing a world-class team. Come join us!
Backend San Francisco
Forward Deployed Engineer San Francisco
Front-End Engineer San Francisco
Solutions Engineer New York, San Francisco, or Remote
Talented, Dedicated Teammates: Our team (https://www.scaleapi.com/blog/scale-engineering-team) is first-class, hailing from Google, Dropbox, OpenAI, Palantir and more. We push each other constantly and strongly value personal growth, as well as camaraderie and friendship. We ship quality code fast, and we take care of each other.
Compelling Technical Challenges: We're focused on building software and processes to automate and speed up the completion of manual tasks that make AI and ML possible. This space is filled with interesting, difficult technical challenges, and our business must tackle both consumer- and enterprise-like dynamics, not to mention make ever-better use of our increasing mass of data over time.
Strong Growth: We're backed by top-level investors in Accel and Index, and we have very strong product-market fit and traction!
Currently our Engineering team is roughly 30 total. Our major departments, as of now, are Data Science/Machine Learning, Frontend (Applications and UI), Backend (generalist, Infra, DevOps) and QA. The teams each have a TL/Manager which you would report to for weekly checkins an d1:1s. Each department has sub teams that have daily standups, project planning, and cycles of execution for production. Every week, there is an entire EPD (engineering, product and design) standup where each team shares the State-Of-The-Union. On Fridays, we have engineering demos, open to all engineers to share what they've been working on with the rest of HQ. This is a great chance to give some insight into our non-technical employees and to provide more context around the work that's going on!
1) We're effectively building Amazon Web Services for people. We provide our customers with a statistically guaranteed high quality of service, making sure we're routing incoming tasks to the correct place and assigning the best possible people to each task.
2) We need to build new Machine Learning models to make the process of solving tasks more efficient. We've built products that integrate ML model outputs with human QA and need to continue developing new models as well as building the backend architectures and user interfaces that best combine humans and ML.
3) We have a lot of customer facing software to build, e.g. building more tools into the customer dashboard, improving API design, client libraries, etc.
4) On our backend we have many challenges to improve the routing of tasks to scalers (people completing the incoming tasks), building systems to staff and de-staff scalers based on their quality over time.
Our customers provide us with a lot of data that needs to be labeled/annotated. In order to do so, we need to build a real-world simulation to make sense of their data to begin with. In effect, we build out an entire 3D world within a browser to simulate the exact conditions our customers were in when gathering their data.
We have a need to build out better visualization tools and dashboards for our customers and human labelers.
We're trying to combine Machine Learning and Human ability into one giant hybrid system. You'd get to work on everything from the frontend UI/UX, to backend infrastructure, to ML prediction systems.
We value mutual respect, collaboration, a problem solving mindset, and a focus on customer needs.
We're a predictably nerdy bunch that enjoys spending time with each other after work at dinner, playing board games, etc.
We care more about getting the right answer/solution than focusing on who came up with that answer/solution. While we do celebrate individual achievements, we love it when we can celebrate team collaboration and victories.
Interested in this company?
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