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 Mercor 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
Mercor 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
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 Mercor’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 Mercor builds benchmarks