Member of Technical Staff - Research Engineer, Post-training
Preference Model· Engineering
San Francisco$200k - $350kFull-timePosted yesterday
About the role
Research-focused role blending machine learning research and engineering to push post-training capabilities of large language models within proprietary RL environments and scalable infrastructure.
Responsibilities
- Train and evaluate models on proprietary RL environments to validate data quality, surface gaps in task coverage, and close the feedback loop between environment design and model capability.
- Architect and optimize RL training infrastructure, from training abstractions to distributed experiment management, using frameworks like Verl, OpenRLHF, or similar. Help scale systems to handle increasingly complex research workflows.
- Design, implement, and test training environments, evaluations, and methodologies for RL agents.
- Profile and optimize training runs end-to-end, from data loading through reward computation, to maximize experiment throughput and shorten the research iteration cycle.
Qualifications
- Industry experience running end-to-end LLM post-training pipelines of models sizes at least 7B in size
- Proficiency in Python and PyTorch or JAX
- Experience with at least one modern RL training framework
- Experience building and operating ML infrastructure at scale
Skills
Required:PythonPyTorch or JAXRL training frameworks
Benefits
- Competitive cash and equity compensation (>90th percentile)
- Ownership and autonomy in a fast moving startup environment
- Opportunity to work with top machine learning engineers
- Health, vision, dental, benefits
- 401K match
- Lunch provided everyday onsite
- Weekly snack orders
- Visa sponsorship & relocation support available
Last updated Oct 25, 2026. Interested? Apply on the company site.
About Preference Model
Preference Model is building the next generation of training data to power the future of AI.
Automated ML research engineeringLLMsRL environmentsbessemer-ai-infrastructure-2026frontier AIreinforcement-learning-platformstraining data
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