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# Research Engineer - Environments, Data and Post-Training

- **Company:** Mercor, Inc.
- **Location:** San Francisco, CA, United States
- **Salary:** $10,000.0 - $15,000.0
- **Contract:** Permanent contract
- **Skills:** Training Data, Application Programming Interfaces (APIs), Artificial Intelligence, Data Structures, Machine Learning, Language Modeling, NoSQL, SQL Databases, Large Language Models, Model Validation, Backend, Data Generation
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/research-engineer-environments-data-and-post-training-mercor-7849318

## About the Role

* 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.

## Description

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., * 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.


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