Research Scientist, APEX Benchmarks

3 дн. назад

200k–500k USD / yearUSAOnsite

Lead the design of next generation APEX benchmarks and expert-built datasets measuring AI's impact on professional work.

Обязанности

We’re looking for a Research Scientist to lead the design of the next generation of APEX benchmarks and the expert-built datasets behind them.
APEX is the AI Productivity Index: a family of benchmarks that measures whether frontier models can do economically valuable professional work. APEX-1 covers single-turn tasks across investment banking, corporate law, consulting and medicine. APEX-Agents tests multi-hour, cross-application agentic work in real tools. APEX-Accounting and APEX-SWE extend that into accounting and real-world software engineering. Every task is written and graded by practicing experts on the platform, and the results are published as papers, open datasets, and public leaderboards that frontier labs watch.
This is a highly visible role at the intersection of research, company strategy, and go-to-market. You’ll decide what to measure next based on where frontier models are actually failing, design the benchmark and its scoring, work with academic and industry partners to build it, then partner with data operations, product and GTM to scale production. You’ll also be a credible technical voice externally — with labs, partners, prospects and the broader research community.
is still early on the research side. Much of the methodology, tooling and publishing practice we need does not exist yet. We’re looking for someone who wants to build it.

Будет плюсом

Ph.D. in machine learning, NLP or a related field; equivalent industry or frontier lab research experience considered.
Publications at top-tier venues (NeurIPS, ICML, ACL, ICLR), especially in evaluation, benchmarking or data-centric AI.
Experience authoring a widely-adopted public benchmark or dataset.
Industry experience on an evaluation, benchmarking or post-training team at a frontier lab.
Domain depth in one of APEX’s professional verticals — finance, law, consulting, accounting, medicine or software engineering.
Experience designing rubrics, model-as-judge pipelines, or human annotation programs at scale.

Условия

Bi-annual performance bonus structure
Generous equity grant vested over 4 years
Up to $15k Relocation bonus
$10K housing bonus (if you live within 0.5 miles of our office)
$1.5K monthly stipend for meals
Free Equinox membership
$200 monthly laundry reimbursement
$200 monthly personal wellness reimbursement
Health, Dental, Vision insurance
401(k) with company match

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.
Benchmark design: Decide what the next APEX benchmark should measure, based on frontier model performance, saturation of existing evals, and where economically valuable work is still out of reach. Own the task taxonomy, difficulty calibration, contamination controls and statistical design.
Dataset design: Design expert-built datasets and grading rubrics at scale — deciding what makes a task hard, what makes a grade defensible, and how to hold quality while thousands of experts produce work in parallel.
Measurement rigor: Set the standard for how we report results: confidence intervals, inter-rater agreement, human vs. model-as-judge calibration, held-out splits, and the failure analysis that explains why a model scored the way it did.
Partnerships: Work with academic collaborators and industry partners (as we did with Cognition on APEX-SWE) to co-design benchmarks and get them adopted.
External research voice: Publish — arXiv papers, open datasets, blog posts, conference talks, leaderboard releases — and represent ’s research in conversations with frontier labs, customers and the press.
Translate results into narrative: Turn benchmark findings into clear arguments about the ROI of expert-curated data, for technical reports, customer conversations and go-to-market material.
Cross-functional work: Partner with data operations, engineering, product and strategy to take a benchmark from design to production, and to surface research findings that shape the company roadmap.
Stay at the frontier: Track the LLM evaluation literature and bring what’s good into how builds benchmarks.
Research background in evaluation: Strong applied or academic research background in LLM evaluation, benchmarking, NLP or a related field, with a track record of rigorous experimental design.
Judgment about what to measure: You can look at a frontier model’s behavior and identify the measurement that would actually be informative, rather than the one that is easiest to build.
Statistical rigor: You reason carefully about sampling, variance, contamination and grader reliability, and you don’t ship a number you can’t defend.
Hands-on: Strong coding skills. You can build an eval harness, run the experiment and analyze the results yourself.
Exceptional communication: You can present complex technical findings clearly to frontier lab researchers and to non-technical audiences, in writing and in person.
Comfort with ambiguity: You’ve operated in fast-moving, cross-functional environments where the problem space is not yet defined.
Interest in the commercial side: Genuine curiosity about GTM strategy, startup dynamics and the business of AI data — this role sits close to all three.
In-person: You are excited to work from our San Francisco office five days a week in a high-intensity, high-ownership environment.
APEX results are read by the labs building the models. You’ll decide what the industry measures next, publish it under your own name, and see it move the conversation within weeks rather than years — with the expert network, capital and engineering support to build benchmarks nobody else can.