Member of Technical Staff - Research Engineer, Post-training

Preference Model; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

Competitive cash and equity (>90th pct

Ownership and autonomy

Lunch onsite

Weekly snack orders

Visa sponsorship & relocation support

Health, vision, dental benefits

401K match

Job summary

Preference Model is building automated ML research engineering to advance RL environments and post-training for large language models. We seek machine learning Research Engineers or Research Scientists to blend research and engineering, implementing novel approaches and shaping research directions.

You will train and evaluate models on proprietary RL environments, architect training infrastructure, and optimize end-to-end runs to accelerate research cycles.

Qualifications

• Experience running end-to-end LLM post-training pipelines of models sizes 7B+.
• Proficiency in Python and PyTorch or JAX.
• Experience with at least one modern RL training framework.
• Experience building and operating ML infrastructure at scale.

Responsibilities

• Train and evaluate models on proprietary RL environments to validate data quality and task coverage.
• Architect and optimize RL training infrastructure and experiment management.
• Design, implement, and test training environments and methodologies for RL agents.
• Profile and optimize training runs end-to-end to increase throughput.

Skills

End-to-end LLM post-training

Python

PyTorch or JAX

RL training framework

ML infrastructure

Tools

Verl

OpenRLHF

Job description

About Us

Preference Model is building automated ML research engineering.
Existing frontier models are brittle when applied to real-world ML tasks. The present bottleneck is the lack of high-quality RL training environments. Our first step is to build RL environments that reflect real-world complexity, with diverse tasks and robust reward functions.

Our founding team has previous experience on Anthropic’s data team building data infrastructure, and datasets behind Claude. We are partnering with leading AI labs to push AI closer to achieving its transformative potential.

About the Role

Models of the future will be able to train themselves on tasks that they are not good at. We are interested in investigating how far we can push the boundaries of self-directed learning. We are looking for machine learning Research Engineers or Research Scientists to push the frontier of post-training on large language models in a role that blends research and engineering, requiring you to implement novel approaches and shape research directions.

What You Will Do:

• Train and evaluate models on our 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 our RL training infrastructure, from training abstractions to distributed experiment management, using frameworks like Verl, OpenRLHF, or similar. Help scale our 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.

What We are Looking For

• 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

You may be a good fit if you also:

• Have experience evaluating model outputs and building reward or evaluation signals

• Stay current on post-training research and can translate papers into running code

• Have strong opinions (loosely held) about how to structure RL training code for reproducibility and fast iteration

• Can balance research exploration with engineering rigor

• Have strong systems design and communication skills

Candidates don't need a PhD or extensive publications. Some of the best researchers have no formal ML training and gained experience building industry products. We believe adaptability combined with exceptional communication and collaboration skills are the most important ingredients for successful startup research.

What We Offer:

• 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

We value diverse perspectives and experiences. If you're excited about this role but don't check every box, we still encourage you to apply.