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Machine Learning Research Engineer/Research Scientist (RL Environments & Post-Training)

preference model • United States
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AI Summary

Seeking ML Research Engineers/Scientists to design, build, and optimize reinforcement learning (RL) environments for training advanced language models. Focus on self-directed learning, reward signal design, and scaling RL infrastructure for large-scale experiments. Requires expertise in LLM post-training pipelines, Python/PyTorch/JAX, and modern RL frameworks.

Key Highlights
Research and engineering role focused on RL environments for superintelligent models
Design, optimize, and scale RL training infrastructure for large-scale experiments
Experience with LLM post-training pipelines (models ≥7B), Python/PyTorch/JAX, and modern RL frameworks required

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Key Responsibilities
Train and evaluate models on proprietary RL environments to validate data quality and close feedback loops
Architect and optimize RL training infrastructure, including distributed experiment management
Design, implement, and test RL training environments, evaluations, and methodologies
Profile and optimize end-to-end training runs for maximum throughput and faster iteration
Technical Skills Required
Python PyTorch/JAX Reinforcement Learning (RL) Frameworks

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Benefits & Perks
Competitive cash and equity compensation (>90th percentile)
Health, vision, dental benefits
401K match
Lunch provided onsite
Weekly snack orders
Visa sponsorship & relocation support

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Nice to Have
Experience evaluating model outputs and building reward/evaluation signals
Current knowledge of post-training research and ability to translate papers into code
Strong opinions on reproducible and iterative RL training code structure
Balancing research exploration with engineering rigor
Strong systems design and communication skills

Job Description

About Us

Preference Model is a superintelligence data research company. We build RL environments for training capable, better-aligned superintelligences.

Almost every aspect of how a model behaves is shaped by the reward signals it's trained on. The biggest problem with AIs today is that they don't always do what we intend. Sometimes that's because they aren't capable enough, and sometimes it's because they aren't aligned. Both problems come down to the training objectives.

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Job Overview
Posted Nov 11, 2026
Employment Full-time
Experience Not Applicable
Location United States
Category Machine Learning
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