Mercor, Inc.
About Mercor
Mercor is training models that predict how well someone will perform on a job better than a human can. Similar to how a human would review a resume, conduct an interview, and decide who to hire, we automate all of those processes with LLMs. Our technology is so effective it’s used by all of the top 5 AI labs. We crossed a $100M revenue run rate and have averaged 59% month over month growth for the last 6 months, making us the fastest growing company in the world. The team is small and we remain extremely profitable because we can’t hire great people as fast as revenue is growing. What you’ll do
In your first year you’ll ship analyses and experiments that move core product metrics—match quality, time-to-hire, candidate experience, and revenue. You’ll: Define north-star and feature-level metrics for our ranking, interview analytics, and payouts systems. Design/run A/B tests and quasi-experiments; turn results into product decisions the same week. Build source-of-truth dashboards and lightweight data models so teams can self-serve answers. Instrument events with engineers; improve data quality and latency from ingestion to insight. Prototype quick models (from baselines to gradient boosting) to improve matching and scoring. Help evaluate LLM-powered agents: design rubrics, human-in-the-loop studies, and guardrail canaries. You’ll thrive here if
You have solid fundamentals (statistics, SQL, Python) and projects you’re proud to demo. You iterate fast—frame the question, test, and ship in days—and care as much about clarity of communication as you do about p-values. Curiosity about LLM evaluation, retrieval, and ranking is a bonus; you’ll learn alongside folks who’ve shipped at Jane Street, Citadel, Databricks, and Stripe. Qualifications
0–2 years in data science/analytics or similar; BS/BA in a quantitative field (or equivalent work). Strong SQL; Python for analysis; comfort with experiment design and causal thinking. Communicates crisply with engineers, PMs, and leadership; turns analysis into action. Nice-to-haves: dbt, dashboarding (Hex/Mode/Looker), marketplace or search/recommendation metrics, LLM/agent evaluation. Compensation
Base cash compensation $130-300k Generous equity grant $20K relocation bonus $10K housing bonus $1K/month food stipend Free Equinox membership Health insurance We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon request.
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Mercor is training models that predict how well someone will perform on a job better than a human can. Similar to how a human would review a resume, conduct an interview, and decide who to hire, we automate all of those processes with LLMs. Our technology is so effective it’s used by all of the top 5 AI labs. We crossed a $100M revenue run rate and have averaged 59% month over month growth for the last 6 months, making us the fastest growing company in the world. The team is small and we remain extremely profitable because we can’t hire great people as fast as revenue is growing. What you’ll do
In your first year you’ll ship analyses and experiments that move core product metrics—match quality, time-to-hire, candidate experience, and revenue. You’ll: Define north-star and feature-level metrics for our ranking, interview analytics, and payouts systems. Design/run A/B tests and quasi-experiments; turn results into product decisions the same week. Build source-of-truth dashboards and lightweight data models so teams can self-serve answers. Instrument events with engineers; improve data quality and latency from ingestion to insight. Prototype quick models (from baselines to gradient boosting) to improve matching and scoring. Help evaluate LLM-powered agents: design rubrics, human-in-the-loop studies, and guardrail canaries. You’ll thrive here if
You have solid fundamentals (statistics, SQL, Python) and projects you’re proud to demo. You iterate fast—frame the question, test, and ship in days—and care as much about clarity of communication as you do about p-values. Curiosity about LLM evaluation, retrieval, and ranking is a bonus; you’ll learn alongside folks who’ve shipped at Jane Street, Citadel, Databricks, and Stripe. Qualifications
0–2 years in data science/analytics or similar; BS/BA in a quantitative field (or equivalent work). Strong SQL; Python for analysis; comfort with experiment design and causal thinking. Communicates crisply with engineers, PMs, and leadership; turns analysis into action. Nice-to-haves: dbt, dashboarding (Hex/Mode/Looker), marketplace or search/recommendation metrics, LLM/agent evaluation. Compensation
Base cash compensation $130-300k Generous equity grant $20K relocation bonus $10K housing bonus $1K/month food stipend Free Equinox membership Health insurance We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon request.
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