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MercorMercorSan Francisco, CA

Research Engineer - Environments, Data and Post-Training

Develops post-training pipelines, RLVR experiments, synthetic data generation, and large-scale LLM evaluation systems to enhance frontier language model performance in tool use, agentic behavior, and reasoning. Requires strong ML experience, coding skills, and research background.

130k – 500k/yr
On-siteML Engineering

About the role

Responsibilities

  • 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.

Benefits

  • Generous equity grant vested over 4 years
  • $10K housing bonus (if you live within 0.5 miles of our office)
  • $1.5K monthly stipend for meals
  • Free Equinox membership
  • Health insurance

Skills

PyTorchMachine LearningLLMsRLHFRlvrSynthetic DataPost-TrainingEvaluation FrameworksSQLNoSQLAPIsCloud PlatformsData StructuresAlgorithmsBackend Systems

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