Engineering Expert, multi-discipline (talent pool). Remote AI-training work for professionals in this field, posted on an expert talent network for AI labs. This is a standing talent pool, not a specific opening: you are contacted when a matching project starts.
Software Engineering Expert (talent pool). Remote AI-training work for professionals in this field, posted on an expert talent network for AI labs. This is a standing talent pool, not a specific opening: you are contacted when a matching project starts.
Electrical Engineering Expert (talent pool). Remote AI-training work for professionals in this field, posted on an expert talent network for AI labs. This is a standing talent pool, not a specific opening: you are contacted when a matching project starts.
Mechanical Engineering Expert (talent pool). Remote AI-training work for professionals in this field, posted on an expert talent network for AI labs. This is a standing talent pool, not a specific opening: you are contacted when a matching project starts.
Machine Learning Expert (talent pool). Remote AI-training work for professionals in this field, posted on an expert talent network for AI labs. This is a standing talent pool, not a specific opening: you are contacted when a matching project starts.
Civil and Survey Engineer (project Vulcan). Remote AI-training work for professionals in this field, posted on an expert talent network for AI labs.
You’ll need: clear open source contributions and a profile to showcase them, such as GitHub (preferably with C++, Python, Java, Go, TypeScript, or Rust). If you don’t, you’ll be assessed through a HackerRank assessment instead. Your tasks: Build reproducible Reinforcement Learning Environments that test an AI model’s ability to fix code, add features, refactor, and optimize performance – including a golden reference solution for each problem
You’ll need: clear open source contributions and a profile to showcase them, such as GitHub (preferably with C++, Python, Java, Go, TypeScript, or Rust). If you don’t, you’ll be assessed through a HackerRank assessment instead. Your tasks: Build reproducible Reinforcement Learning Environments that test an AI model’s ability to fix code, add features, refactor, and optimize performance – including a golden reference solution for each problem
micro1 is partnering with a leading AI lab to hire data scientists and quantitative professionals from top companies. You’ll bring rigor to how AI models handle statistics, machine learning, experimentation, and quantitative reasoning.
We’re looking for data scientists, machine learning practitioners, and applied mathematics experts with competitve Data Science experience. (Kaggle, DrivenData, etc.) You’ll draw on your competition experience, mathematical reasoning, and programming skills to create tasks that require more than a plausible answer. Each task should have a reproducible environment, a verified reference solution, and reliable checks for successful completion.