Member of Technical Staff - Machine Learning Capabilities, New Graduates
Preference Model· Engineering
San Francisco$165k - $200kFull-timePosted 3mo ago
About the role
New graduate Machine Learning Engineers to design and build reinforcement learning environments to safely advance model capabilities in ML research and engineering. Role blends research and engineering with full ownership of RL environments, experiments, and production training runs within the Capabilities org.
Responsibilities
- Design and build RL environments and reward schemes that produce clean, learnable signals for frontier models on ML research and engineering tasks.
- Build deep expertise across the frontier of ML research, training, and inference infrastructure.
- Collaborate with others to brainstorm and create new ideas and tools to improve the environment building process.
Qualifications
- Strong ML fundamentals and broad research interests.
- Expert knowledge in an active DL/ML research area, with publications or public code to show for it.
- Research experience (PhD) is a strongly preferred.
- Deep understanding of transformer internals.
- Proficiency in Python, Numpy, and systems programming; ideally PyTorch or JAX.
- Smart problem solvers who take ownership and drive solutions end-to-end.
- Passion for staying current with the rapidly evolving ML infrastructure landscape.
- Ability to meet throughput expectations and respond quickly to feedback.
Skills
Required:Machine Learning fundamentalsTransformer internalsPythonResearch experience / publications
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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