Scale is building out one of the largest hybrid human-machine systems. Scale’s self-regulating system automatically trains workers and ensures continuous quality and optimal allocation. We have thousands of human labelers that complete millions of tasks a month, and that comes with a host of interesting technical challenges. From product to systems to infrastructure engineering, we’re tackling it all to accelerate the development of AI.
This role could be a fit if you have:
- Use models to estimate the quality of tasks and labelers, and guarantee quality on requests at large scale.
- Properly route tasks from customers to labelers for low turnaround and high accuracy.
- Build methods to automatically measure and train labelers and optimally match labelers to tasks based on performance.
- Build robust machine learning models to automate requests and improve our labelers’ efficiency.
- 3-7+ years of industry experience as a software engineer post graduation
- Systems engineering experience with real-time and distributed system architecture.
- Experience building systems that process large volumes of data.
- Experience or interest in using the following: AWS, Typescript, Node, Mongo, MLflow, Spark, Presto, Python (note that we are mostly language-agnostic and are open to using whatever is the best tech for the problem at hand)
- Mentored and grown members of your team or been a tech lead on large projects
- At least a Bachelor’s degree (or equivalent) in a relevant field.
At Scale, our mission is to accelerate the development of Machine Learning and AI applications across multiple markets. Our first product is a suite of APIs that allow AI teams to generate high-quality ground truth data. Our customers include OpenAI, Zoox, Lyft, Pinterest, Airbnb, nuTonomy, and many more
Scale is an equal opportunity employer. We aim for every person at Scale to feel like they matter, belong, and can be their authentic selves so they can do their best work. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.