Machine Learning Fellow - Human Frontier Collective (canada)

Scale AI Scale AI · Data AI · Remote · Human Frontier Collective

This role is for a Machine Learning Fellow focused on evaluating, interpreting, and optimizing advanced generative AI systems. The fellow will engage in ML projects, contribute to research publications, and collaborate with AI labs and platforms. The role requires a PhD or postdoctoral degree in a related field and experience with Python and ML frameworks.

What you'd actually do

  1. ML Projects: Get invited to engage in high-impact projects with our partnered AI labs and platforms. Help models understand real-world deep learning workflows by designing, reviewing, and optimizing PyTorch models, evaluating complex ML code and AI-generated implementations for efficiency and correctness, and advising on GPU optimization, scaling, and trade-offs.
  2. HFC Community: Beyond the work, you’ll become part of a supportive, interdisciplinary network of innovators and thought leaders committed to advancing frontier AI across domains.
  3. Contribute to Research Publications: Collaborate with Scale’s research team to co-author technical reports and research papers—boosting your academic visibility and professional recognition (e.g., [_SciPredict_](https://scale.com/research/scipredict), [_PropensityBench_](https://scale.com/research/propensitybench), [_Professional Reasoning Benchmark_](https://scale.com/research/prbench)).

Skills

Required

  • Python
  • PyTorch
  • TensorFlow
  • evaluate complex ML code
  • advise on GPU optimization, scaling, and trade-offs

Nice to have

  • AWS
  • Docker
  • Langchain
  • MLOps tools

What the JD emphasized

  • authorized to work in Canada
  • PhD or postdoctoral degree in Computer Science, Computer Engineering, or a related field
  • 1-3+ years of experience as a Machine Learning Engineer or Data Scientist

Other signals

  • designing, reviewing, and optimizing PyTorch models
  • evaluating complex ML code and AI-generated implementations
  • advising on GPU optimization, scaling, and trade-offs
  • collaborate with Scale’s research team to co-author technical reports and research papers