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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.
Job Responsibility:
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
Requirements:
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
What we offer:
Generous equity grant vested over 4 years
A $20K relocation bonus (if moving to the Bay Area)
A $10K housing bonus (if you live within 0.5 miles of our office)