Research Engineer

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.

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.

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.