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Mercor
San Francisco, California, United States
Source: Mercor careers · View original posting
From Mercor's posting. “We” and “our” refer to the employer.
Mercor'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. Mercor's APEX benchmark family measures AI's real-world impact on professional work.
Mercor 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.
Mercor 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. Mercor 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.
As a Data Science Intern at Mercor, you’ll join a fast-moving, metrics-driven engineering team that powers critical decisions across the company. You’ll analyze data that directly impacts ranking, hiring efficiency, candidate experience, and revenue. From day one, you’ll work with real datasets, ship insights used by product and engineering, and prototype models that improve how we match talent to AI companies.
You’ll work closely with engineers, PMs, and leadership, designing experiments, evaluating LLM-powered systems, and building the foundations of data integrity and visibility across the platform. You’ll move quickly while maintaining a high bar for analytical rigor, clarity, and statistical correctness.
At the end of the process, you’ll be team-matched to where you can have the most impact, on one of the following:
Defining north-star metrics and feature-level KPIs for ranking, interview analytics, and payouts systems.
Designing and running A/B tests and quasi-experiments; translating results into product decisions within days.
Building dashboards and lightweight data models that empower teams to self-serve insights.
Instrumenting events with engineers and improving data quality, observability, and latency.
Prototyping models (from baselines to gradient boosting) to improve matching and scoring systems.
Evaluating LLM-powered agents through rubric design, human-in-the-loop experiments, and guardrail canary testing.
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