Research Engineer Intern - Environments, Data and Post-Training
At a glance
Junior AI Research role at Mercor. San Francisco · internship.
Pay not stated AI Research salary
aimlroles summary, based on the employer's posting.
What you'll do
- Run post-training and RLVR experiments to test how rewards, training approaches, and datasets affect model results
- Shape rewards and improve algorithms to strengthen language models' tool use, agent behavior, and real-world reasoning
- Measure dataset quality and usefulness, including performance gains on benchmarks
- Build data-generation and augmentation pipelines, plus rubrics and evaluators for training decisions
- Operate large-scale model evaluation systems and work with researchers and training-data experts
What you bring
- You have applied research experience focused on post-training, model evaluation, or both
- You can code well and have hands-on experience working with machine learning models
- You understand data structures, algorithms, backend systems, and core software engineering
- You can work with APIs, SQL or NoSQL databases, and cloud platforms
Who this fits
This internship suits someone who can reason carefully about model behavior, experiment results, and data quality in a fast-moving research setting. It is based in San Francisco, with in-person work five days a week and optional remote Saturdays.
From the employer
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 Engineer at Mercor, you’ll work at the intersection of engineering and applied AI research. You’ll contribute directly to post-training and RLVR, synthetic data generation, and large-scale evaluation workflows that meaningfully impact frontier language models.
Your work will be used to train large language models to master tool use, agentic behavior, and real-world reasoning in real-world production environments. You’ll shape rewards, run post-training experiments, and build scalable systems that improve model performance. You’ll help design and evaluate datasets, create scalable data augmentation pipelines, and build rubrics and evaluators that push the boundaries of what LLMs can learn.
What You’ll Do
Work on post-training and RLVR pipelines to understand how datasets, rewards, and training strategies impact model performance.
Design and run reward-shaping experiments and algorithmic improvements (e.g., GRPO, DAPO) to improve LLM tool-use, agentic behavior, and real-world reasoning.
Quantify data usability, quality, and performance uplift on key benchmarks.
Build and maintain data generation and augmentation pipelines that scale with training needs.
Create and refine rubrics, evaluators, and scoring frameworks that guide training and evaluation decisions.
Build and operate LLM evaluation systems, benchmarks, and metrics at scale.
Collaborate closely with AI researchers, applied AI teams, and experts producing training data.
Operate in a fast-paced, experimental research environment with rapid iteration cycles and high ownership.
What We’re Looking For
Strong applied research background, with a focus on post-training and/or model evaluation.
Strong coding proficiency and hands-on experience working with machine learning models.
Strong understanding of data structures, algorithms, backend systems, and core engineering fundamentals.
Familiarity with APIs, SQL/NoSQL databases, and cloud platforms.
Ability to reason deeply about model behavior, experimental results, and data quality.
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
Real-world post-training team experience in industry (highest priority).
Publications at top-tier conferences (NeurIPS, ICML, ACL).
Experience training models or evaluating model performance.
Experience in synthetic data generation, LLM evaluations, or RL-style workflows.
Work samples, artifacts, or code repositories demonstrating relevant skills.
Benefits
Health Benefits eligible at 30+ hours a week
Overtime eligible at 1.5x from 40 to 55 hours; 2x over 55 hours-week.
$1.5K monthly stipend for meals
Relocation & Housing Support
Free Equinox membership
$200 monthly laundry reimbursement
$200 monthly personal wellness reimbursement
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