Machine Learning Fellow - Human Frontier Collective (UK)

scaleai; United States; Remote; USD 83,000 - 165,000; Part time

Job summary

Human Frontier Collective (HFC) seeks researchers to join its exclusive network for AI frontier projects. This fully remote 1099 contractor role involves collaborating with Scale AI and affiliated labs to build, review, and optimize AI systems, with opportunities to co-author technical reports and papers.

Ideal candidates have a PhD or postdoctoral experience in ML/CS or senior industry expertise, strong Python and ML framework skills (PyTorch, TensorFlow), and familiarity with AWS, Docker,

Job description

About the Collective

The Human Frontier Collective (HFC) 's mission is to bring the world's best minds to shape the future of AI - because the future of AI depends not only on powerful models, but on the people who teach them to think. We bring together researchers, academics, and domain leaders across 70+ fields - over 90% of members hold doctorates - to shape how frontier AI systems are built, evaluated, and governed. HFC members have since co-authored published work including SciPredict , PropensityBench , and Professional Reasoning Benchmark .

Why join the HFC

HFC centers on three core pillars: the network itself, participation in selected AI/ML projects, and opportunities to publish with our research team.

Join a unique and exclusive expert network: You'll become a part of an interdisciplinary community, consisting of over 90% doctorates and field leaders representing 70+ fields from the leading institutions. We're a collective of top innovators and thought leaders committed to advancing frontier AI to power the world's most important decisions.

Participate in AI/ML projects: Beyond the collective network, you'll be regularly invited to work on high-impact projects with Scale AI and its affiliated lab and platform: building AI safety and policy guardrails, helping 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 more.

Co-author selected research publications: Collaborate with Scale's research team to co-author technical reports and research papers-boosting your academic visibility and professional recognition.

Who should apply

• Background : PhD, or postdoctoral research experience, in Machine Learning, Computer Science, or a related field; or senior industry experience in Machine Learning or a related field.
• Skills : Strong proficiency in Python and modern ML frameworks (PyTorch, TensorFlow). Experience with cloud infrastructure (AWS) and MLOps tools (Docker) and LLM frameworks (LangChain) is a plus.
• Professional Mindset : Detail-oriented, innovative thinker with a passion for applied AI research and a commitment to collaboration.

How it works

• Duration : This is a fully remote opportunity with no fixed end date; engagements continue as long as there's mutual interest. This is a 1099 independent contractor engagement.
• Work authorization : We do not sponsor visas for Fellows. To participate in the HFC Fellows program, you need to have or independently obtain full-time work authorization in the country they reside in.
• Community Network : Once you've received an invitation to join the HFC, you'll also gain access to our newsletters, discussion channels, virtual sessions, IRL events, and much more.
• Projects Logistics :
  • Project selection: You'll regularly get matched to carefully selected projects from our partners. Depending on the partner, projects range from evaluating AI models to designing experiments, building RL environments, co-authoring research papers, and more.
  • Flexible schedule: There's no minimum commitment - you decide whether to take on a project. Most fellows spend 10-25 hours per week, on a schedule they set themselves.
  • Competitive pay: Project pay rates vary across platforms and depend on a number of factors, including but not limited to: projects, scope, skillset, and location. You'll receive the pay information upon receiving project matching notifications.