Researcher, Post Training

Responsibilities

• Own the full lifecycle of the post-training pipeline, from data curation and training runs through evaluation and deployment.
• Apply and adapt reinforcement learning, preference optimization, and supervised fine-tuning methods to improve models for code generation, user-intent reasoning, and reliable agent behavior.
• Build evaluation and experimentation infrastructure covering helpfulness, safety, latency, and reliability.
• Develop and operate production systems for large-scale training jobs, including GPU orchestration and data pipelines.
• Collaborate with agent, product, and infrastructure engineers to turn model gains into product improvements.
• Investigate and resolve failures end to end, including training recipes, data issues, and serving regressions.
• Read research papers, run experiments, and move promising research into production quickly.

Requirements

• Hands-on experience running post-training jobs on large language models, including RFT/RLVR, preference optimization, or similar methods.
• Strong production software engineering skills.
• Fluency in at least one major ML framework, such as PyTorch or JAX.
• Experience with distributed training setups and GPU clusters.
• Understanding of the mathematics behind preference optimization, reward modeling, and alignment techniques.
• Experience building evaluation systems that measure real-world quality beyond benchmark scores.
• Ability to trace model-quality regressions from user-facing symptoms through serving, inference, and training.
• Strong execution and focus on shipping improvements to users.

Nice-to-haves

• Experience with code-generation or agentic use cases.
• Experience putting post-trained models into the hands of real users at scale.
• Ownership of the full loop: data curation, training, evaluation, deployment, and production monitoring.
• Experience rapidly prototyping ideas from research papers.
• Experience with speculative decoding or similar model-efficiency techniques.
• Strong opinions on evaluation methodology and experience building evaluations that predict user satisfaction.
• Meaningful contributions to the open-source ML ecosystem.