5-7 years of applied research experience in post-training or model evaluation
Proficient in coding with hands-on experience in machine learning models
Strong grasp of data structures, algorithms, and backend systems
Familiar with APIs, SQL/NoSQL databases, and cloud platforms
Ability to analyze model behavior and data quality
Willingness to work in-person in San Francisco, five days a week
Responsibilities
Develop post-training and RLVR pipelines to enhance model performance
Design and execute reward-shaping experiments and algorithmic improvements
Assess data usability and performance uplift on benchmarks
Create scalable data generation and augmentation pipelines
Establish and refine evaluation rubrics and scoring frameworks
Build and manage LLM evaluation systems and metrics
Collaborate with AI researchers and applied AI teams
Benefits
$1.5K monthly stipend for meals
Free Equinox membership
$200 monthly laundry reimbursement
$200 monthly personal wellness reimbursement
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.
