STEM Fellow – Human Frontier Collective (UK)
Scale AI · Remote · Remote
Job Description
Location: United Kingdom
Department: Human Frontier Collective
Job Type: Fully Remote | Independent Contractor (1099)
Contract Duration: Approximately 6 months, with potential extension
Join the Team Shaping the Future of AI at Scale
The Human Frontier Collective (HFC) is seeking exceptional STEM researchers, postdoctoral scholars, and academic professionals to contribute to frontier AI research and evaluation initiatives. This is an opportunity to collaborate with leading AI laboratories and interdisciplinary experts working on the next generation of advanced generative AI systems.
Please note that applicants must be authorized to work in the country where they reside.
About the Program
The Human Frontier Collective Fellowship brings together top researchers and domain specialists to collaborate on high-impact initiatives shaping the future of artificial intelligence. Fellows apply their academic and professional expertise to help design, evaluate, and interpret advanced AI systems while gaining exposure to cutting-edge research and an international network of thought leaders.
As an HFC Fellow, you will work alongside researchers, scientists, and AI experts dedicated to advancing responsible and human-centered AI technologies.
Key Responsibilities
Collaborative AI Research & Evaluation
- Participate in high-impact projects with partnered AI labs and technology platforms.
- Design complex, domain-specific problems and datasets to evaluate AI model performance.
- Provide expert insights that improve AI reasoning, accuracy, reliability, and interpretability.
- Contribute to the development of advanced generative AI systems across scientific and technical domains.
Interdisciplinary Community Engagement
- Join a collaborative global network of innovators, researchers, and industry leaders.
- Engage with experts from diverse academic and professional backgrounds working at the frontier of AI advancement.
Research Publications & Technical Contributions
- Collaborate with Scale’s research teams on technical reports and research publications.
- Contribute to projects such as:
- SciPredict
- PropensityBench
- Professional Reasoning Benchmark
- Enhance your academic visibility and professional recognition through impactful research contributions.
Who Should Apply
Educational Background
Ideal candidates include:
- PhD holders
- Postdoctoral researchers
- Professors
- Advanced researchers with expertise in STEM-related disciplines, including:
- Computer Science
- Machine Learning
- Physics
- Biology
- Chemistry
- Mathematics
- Humanities
- Other related fields
Professional Qualities
Successful candidates are:
- Detail-oriented and analytical
- Innovative thinkers with strong research capabilities
- Passionate about applied AI research
- Collaborative and adaptable in interdisciplinary environments
Why Join the Human Frontier Collective?
Professional Growth
Expand your influence through:
- Research collaborations
- Advisory opportunities
- High-impact AI review projects
- Exposure to pioneering AI applications in science and technology
Elite Global Network
Collaborate with a world-class community of academics and experts dedicated to advancing responsible AI research. Approximately 80% of HFC members come from leading global institutions.
Flexible Work Structure
- Fully remote opportunity
- Flexible 10–40 hour work weeks
- Schedule designed around your availability and commitments
Competitive Compensation
Project pay rates vary depending on:
- Project scope
- Technical specialization
- Skillset
- Geographic location
- Platform requirements
Application Process
1. Apply
Applications are reviewed on a rolling basis.
2. Complete a Challenge
Selected applicants will complete a short task designed to assess domain expertise and simulate HFC project work.
3. Interview
Shortlisted candidates will discuss:
- Research background
- Professional experience
- Alignment with the mission of advancing human-centered AI
4. Join the Collective
Successful applicants will receive an invitation to join the Human Frontier Collective Fellowship.
Important Notice
HFC maintains a 90-day waiting period before reconsidering candidates for the same role. This policy supports a fair and thorough evaluation process for all applicants.
About Scale
Scale AI develops reliable AI systems that power some of the world’s most important decisions. The company provides high-quality data and full-stack AI technologies used by leading enterprises, research institutions, and governments worldwide.
Scale works with organizations including:
- Meta
- Cisco
- DLA Piper
- Mayo Clinic
- Time Inc.
- Government agencies including the U.S. Army and Air Force
The company continues to expand its global talent network to accelerate the development and deployment of impactful AI applications.
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LIKELY QUESTIONS - Can you walk me through your research background and explain how it prepares you to design domain-specific evaluations for advanced generative AI systems? - Tell me about a time you created a benchmark, dataset, rubric, or experimental framework to assess the performance of a complex system. - How would you evaluate whether a frontier model is truly reasoning well in your domain, rather than just pattern-matching? - This role involves improving accuracy, reliability, and interpretability. Which of those areas have you worked on most directly, and what methods did you use? - How do you balance scientific rigor with the practical constraints of fast-moving applied AI projects? - Describe a project where you collaborated across disciplines or with non-specialists. How did you make your expertise useful to the broader team? - If asked to contribute to projects like SciPredict, PropensityBench, or a professional reasoning benchmark, how would you approach task design and quality control? - This is a remote, flexible, contractor role. How do you manage ambiguity, shifting priorities, and independent ownership while maintaining high-quality output? BEHAVIOURAL QUESTIONS - Tell me about a time you had to solve an ambiguous research problem with limited guidance. Model approach: Situation: Briefly describe an open-ended research or evaluation problem with unclear success criteria. Task: Clarify what you needed to deliver and why it mattered. Action: Explain how you framed hypotheses, defined evaluation criteria, gathered evidence, aligned stakeholders, and iterated. Result: Quantify the outcome, such as improved benchmark quality, clearer decision-making, publication progress, or adoption by the team. - Describe a time you found a flaw in a dataset, benchmark, or methodology. Model approach: Situation: Introduce the dataset or evaluation pipeline and the hidden issue. Task: State your responsibility for validating quality or reliability. Action: Show how you detected the flaw, investigated root cause, proposed corrections, and communicated implications diplomatically. Result: Emphasize better validity, reduced bias, stronger reproducibility, or prevention of incorrect conclusions. - Give an example of working with people from different disciplines or backgrounds. Model approach: Situation: Set up a project involving researchers, engineers, product teams, or domain experts. Task: Explain your role in bridging technical and non-technical perspectives. Action: Describe how you translated concepts, aligned on goals, handled disagreements, and built a shared framework. Result: Highlight smoother collaboration, better project outcomes, or a stronger final deliverable. - Tell me about a time you had to deliver high-quality work under tight time constraints. Model approach: Situation: Describe a deadline-driven project with meaningful stakes. Task: Define what had to be completed and the quality bar required. Action: Explain prioritization, risk management, scope control, review steps, and how you maintained rigor despite speed. Result: Share measurable impact, such as on-time delivery, stakeholder satisfaction, or successful deployment/publication. SMART QUESTIONS TO ASK - How do HFC Fellows typically divide their time between benchmark design, direct model evaluation, writing technical reports, and broader collaboration with partner labs? - What distinguishes the most successful Fellows in this program, especially in the first 60 to 90 days? - How are project quality and impact assessed for contractors in a flexible 10 to 40 hour per week structure? - To what extent can Fellows shape the research agenda or propose new evaluation ideas based on their domain expertise? - For projects tied to publications or technical reports, how are authorship, attribution, and visibility typically handled? RED FLAGS TO WATCH FOR - Vague answers about scope, hours, or compensation structure for the contractor arrangement, especially if expectations sound full-time while pay and stability remain unclear. - No clear explanation of how quality is reviewed, how success is measured, or who provides feedback, which can signal disorganized project management in a remote setting. - Overemphasis on speed and volume without discussion of research rigor, responsible AI, or evaluation validity, which would conflict with the human-centered mission.
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Adjacent Career Paths
Roles you'd also qualify for based on this posting's requirements:
- AI Research Evaluator — This role closely matches designing benchmarks, datasets, and assessments for advanced AI systems.
- Machine Learning Research Scientist — A strong STEM researcher would be well suited to contribute technical expertise to applied ML and generative AI projects.
- Research Scientist, Responsible AI — The fellowship's focus on reliability, interpretability, and human-centered AI aligns with responsible AI research work.
- Benchmarking and Evaluation Scientist — Candidates from this post are qualified to build domain-specific tests and analyze model performance across technical areas.