USAResearch Fellowship — APEX
1 нед. назад
40k–80k USDUSARemote
Research fellowship focused on building next generation AI benchmarks with domain experts.
Условия
- 3 month stipend of $40,000 or 6 month stipend of $80,000
- Unlimited API credits, plus a dedicated budget for GPU compute and paid expert/human-data time
- Weekly 1:1 mentorship with a member of the APEX research team, plus regular access to the broader research org
- Access to frontier model APIs, ’s internal evaluation infrastructure, and — where appropriate — real enterprise evaluation problems from ’s customers
- Optional desk in ’s San Francisco office for fellows who want to be in person
- Introductions to ’s network of researchers across frontier labs and academia
- Standout fellows are considered for a full-time offer on the APEX research team at the end of the fellowship
Другое
- 's mission is to organize human intelligence to power the AI economy. We're a leading AI data company, building the layer between human expertise and frontier models. Millions of domain experts on the platform are paid over $4 million per day to train frontier AI models. 's APEX benchmark family measures AI's real-world impact on professional work. Enterprise brings this same infrastructure to Fortune 500 companies: helping companies capture how their best people actually work, translating that expertise directly back into agents.
- is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. is a profitable Series C company valued at $10 billion. We work in-person five days a week in our San Francisco, NYC, or London offices.
- ’s APEX benchmark family measures whether frontier AI models can actually do economically valuable work: multi-hour agentic tasks in investment banking and corporate law, real professional accounting workflows, real-world software engineering, and graduate-level science. Every APEX benchmark is built and validated with ’s network of domain experts — not written from a textbook.
- The Research Fellowship funds people to build the next generation of benchmarks and evaluation techniques. You pitch a benchmark or eval methodology you want to build — a new domain, a harder task format, a better way to measure agentic reliability — and if selected, you get the time, compute, expert labor, and mentorship to design, implement, and release it end to end.
- You’ll work directly with the APEX research team, get access to real enterprise evaluation problems from ’s Fortune 500 and frontier-lab partners, and see your benchmark shape how the industry measures AI capability.
- Duration: 3–6 months, rolling admission
- Commitment: minimum 30 hours/week; full-time preferred
- Location: remote, or in-person at ’s San Francisco office
- Admission: apply with a specific benchmark or eval technique you want to build — the fellowship is funded around your pitch, not a generic research rotation
- Propose and scope a new benchmark or evaluation technique in a domain APEX doesn’t yet cover well, or a meaningfully harder version of one it does.
- Design task specifications and grading rubrics in partnership with ’s network of vetted domain experts — lawyers, accountants, engineers, scientists, and consultants.
- Build and validate the benchmark: pilot tasks, calibrate scoring, and stress-test for contamination and gameable shortcuts.
- Run frontier models against your benchmark and analyze where and why they fail.
- Publish your results — as a paper, an open dataset, a new leaderboard on APEX, or a methodology the APEX team adopts internally.
- Partner with ’s research and engineering teams to fold what you learn back into APEX’s public benchmark family.
- Long-horizon, multi-app agentic tasks in professional services (law, finance, consulting) — extending APEX-Agents
- Real-world software engineering evaluation beyond issue resolution — extending APEX-SWE
- Professional accounting and finance workflows — extending APEX-Accounting
- AI-for-Science evals: research-level mathematics, biology, materials science, and theoretical physics
- Novel evaluation methodology: contamination resistance, rubric design, human-vs-model grading agreement, cost-adjusted scoring
- Strong pitches outside this list are welcome — we fund the best ideas, not the closest fit to a template.
- Genuine interest in evaluation as a research discipline — not just a stepping stone to a model-building role.
- Background in CS, ML, statistics, or an adjacent field (measurement, psychometrics, HCI, social science); no requirement to have published in ML venues.
- A specific, well-scoped idea for a benchmark or eval technique you want to build — the fellowship is built around your pitch.
- Comfortable in a startup environment: fast iteration, direct access to real customer problems, less hand-holding than an academic lab.
- Able to commit at least 20 hours/week for the duration of the fellowship — during a leave, over a summer, or a flexible stretch of a PhD.
- Bonus: experience with agentic evaluation, RL environments, or domain expertise in law, finance, medicine, or a scientific field.