Strong applied research background in post-training or model evaluation
Proficiency in coding with hands-on experience in machine learning models
Solid understanding of data structures, algorithms, and core engineering principles
Familiarity with APIs and SQL/NoSQL databases
Ability to analyze model behavior and data quality
Willingness to work in-person in San Francisco five days a week
Responsibilities
Conduct experiments on post-training and RLVR pipelines to enhance model performance
Design and run rewards-shaping experiments and algorithmic improvements
Assess data usability and performance uplift across benchmarks
Develop scalable data generation and augmentation pipelines
Create and refine evaluators and scoring frameworks
Build LLM evaluation systems and benchmarks at scale
Collaborate with AI researchers and applied teams in a dynamic research environment
Benefits
Generous equity grant vested over 4 years
$20K relocation bonus for new Bay Area residents
$10K housing bonus for proximity to the office
$1K monthly stipend for meals
Free Equinox membership
Health insurance
Full Job Description
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
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)
