Research Scientist - Environments, Data and Post-Training
Company websitemercor.com (Mercor, opens in new tab)
Base salary $250K to $500K
Employer-published annual base salary, USD. Advertised base range in San Francisco, CA
You apply on the employer's own site. Six Figure Work does not handle applications.
- LocationSan Francisco, CA
- WorkplaceOn-site
- Status at last checkSource accepting applications when checked
- Last checked (1 h ago) · observed since
About this job
About Mercor
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.
About the Role
As a Research Scientist at Mercor, you will work at the intersection of research and engineering on frontier post-training. You will develop new training and evaluation methods, test them through rigorous experiments, and implement successful approaches at scale.
Working with researchers, engineers, domain experts, and customers, you will investigate how data, rewards, environments, and optimization methods shape model behavior. Your work will influence frontier models, Mercor’s products, and the broader research community through releasing blogposts, technical reports, and papers.
What You’ll Do
Implement novel post-training methods that improve model reasoning, tool use, and agentic behavior.
Develop new training recipes for frontier open models.
Design and run experiments across datasets, reward functions, environments, and optimization strategies, including methods such as GRPO and DAPO.
Build reinforcement learning with verifiable rewards (RLVR) and other post-training pipelines at scale.
Investigate model capabilities and failure modes, then develop targeted training interventions.
Create methods for measuring data quality, usability, and causal impact on model performance.
Build scalable pipelines for data generation, filtering, augmentation, and selection.
Develop rubrics, evaluators, benchmarks, and scoring systems that inform training decisions.
Translate open-ended research questions into rigorous experiments and production systems.
Collaborate with researchers, applied AI teams, engineers, and domain experts producing training data.
Contribute to open-source post-training tools and research.
What We’re Looking For
Demonstrated experience training and evaluating machine learning models.
A strong research record in post-training, reinforcement learning, language-model evaluation, data-centric ML, or a closely related field.
Ability to reason rigorously about model behavior, experimental results, and data quality.
Strong programming skills and experience implementing machine learning systems.
Knowledge of the current AI research landscape and important open problems.
Excitement to work in person in San Francisco, five days a week (with optional remote Saturdays), and thrive in a high-intensity, high-ownership environment.
Nice To Have
Experience on an industry post-training or frontier-model team.
Main authorship of publications at top-tier conferences (NeurIPS, ICML, ACL).
Experience with synthetic-data generation
Experience building large-scale evaluation or data-generation infrastructure.
Solid foundations in distributed or backend systems, and experimental design.
Familiarity with APIs, databases, and cloud infrastructure.
Benefits
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
Source: Mercor on Ashby · View original posting (opens in new tab)
Pay in context
This role's base floor ($250,000) is above 80% of the 1,485 Data & ML roles in California listed on this site; 19 others list the same floor. Their median floor is $190,000; the middle half runs from $160,000 to $229,900.
Only jobs on this site count, and each one publishes a base salary of $100,000 or more, so this compares six-figure jobs with each other, not with the whole market. Computed Nov 6, 2026.
This listingResearch Scientist - Environments, Data and Post-Training$250K to $500K
Engineering Manager, Applied AI$250K to $400K
Engineering Manager, Fraud & Compliance$250K to $400K
Analytics Engineer$250K to $400K
Software Engineer, Search Systems - Code Data$250K to $500K
Partnerships Lead, AI Infrastructure & Post-Training$250K to $350K
Partnerships Lead, Consulting & SI Partners$250K to $350K
Chart as a table
| Role | Published base |
|---|---|
| Research Scientist - Environments, Data and Post-Training (this listing) | $250K - $500K |
| Engineering Manager, Applied AI | $250K - $400K |
| Engineering Manager, Fraud & Compliance | $250K - $400K |
| Analytics Engineer | $250K - $400K |
| Software Engineer, Search Systems - Code Data | $250K - $500K |
| Partnerships Lead, AI Infrastructure & Post-Training | $250K - $350K |
| Partnerships Lead, Consulting & SI Partners | $250K - $350K |
Observed changes
- Began meeting the published-salary criteria
- First observed on the employer's board
- Observed since
- Employer-stated posting date
- 2026-09-18
Where this pay comes from
Location as the employer wrote it: San Francisco
USD 250,000 – 500,000 · 1 YEAR · Salary
- Method
- Structured pay field published by the employer
- Checked
- Rule set
- us-base-v2
The employer's job board publishes this pay as a dedicated data field. We read the field directly; no interpretation was needed.
What we verified
- The pay figures come from the employer's own listing, quoted exactly above.
- The pay is an annual base salary in US dollars with a minimum of at least $100,000.
- The role was publicly listed on the employer's job board when last checked.
We do not verify what the employer will actually offer, whether the employer is still recruiting for the role, or anything beyond what the employer published. Full methodology.
