Research Engineer Intern - Environments, Data and Post-Training

Mercor; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

Health benefits

Overtime policy (1.5x 40–55h; 2x >55h)

Meals stipend $1.5K/mo

Relocation & Housing support

Equinox membership

Laundry reimbursement

Wellness reimbursement

Job summary

Mercor is seeking a Research Engineer in San Francisco with a strong applied research background to advance post-training and evaluation for frontier language models. You will contribute to RLVR, synthetic data generation, and large-scale evaluation workflows that directly impact model performance.

You will design experiments, build scalable data pipelines, and collaborate with researchers to push the boundaries of LLM tool-use and real-world reasoning.

Qualifications

• Strong applied research background with a focus on post-training and/or model evaluation.
• Strong coding proficiency and hands-on experience with ML models.
• Solid 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, results, and data quality.

Responsibilities

• Work on post-training and RLVR pipelines to understand how datasets, rewards, and training strategies affect model performance.
• Design and run reward-shaping experiments and algorithmic improvements (e.g., GRPO, DAPO) to improve LLM tool-use and 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 to guide training and evaluation decisions.
• Build and operate LLM evaluation systems, benchmarks, and metrics at scale.
• Collaborate with AI researchers, applied AI teams, and training data experts.

Skills

Applied research

Coding proficiency

Data structures

APIs

SQL/NoSQL

Cloud platforms

Model reasoning

Job description

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