Machine Learning Fellow - Human Frontier Collective (us)

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

This role involves applying academic and professional expertise to design, evaluate, and interpret advanced generative AI systems. The fellow will work on ML projects, optimize PyTorch models, evaluate ML code, advise on GPU optimization, and contribute to research publications and technical reports.

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, PropensityBench, Professional Reasoning Benchmark).

Skills

Required

  • Python
  • PyTorch
  • TensorFlow
  • ML frameworks

Nice to have

  • AWS
  • Docker
  • Langchain
  • MLOps tools

What the JD emphasized

  • 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
  • Strong proficiency in Python and modern ML frameworks (PyTorch, TensorFlow)
  • Experience with cloud infrastructure (AWS) and MLOps tools (Docker, Langchain) is a plus

Other signals

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