Member of Technical Staff - AI/ML Infrastructure Engineer, RL Post-training

Preference Model • San Francisco, CA

6d • $200,000 - $350,000

About The Position

Preference Model is a superintelligence data research company that builds reinforcement-learning environments to train capable, better-aligned superintelligences. The company researches how to build better RL environments and has built environments for several frontier labs. It is backed by $16M in seed funding led by a16z, and its founding team has experience building data infrastructure and datasets behind Claude.

The role builds the infrastructure and systems that power frontier post-training research on large language models. The engineer will design and scale compute, scheduling, and data infrastructure for in-house RL environments; develop ML framework primitives and internal tooling; and build evaluation, benchmarking, monitoring, logging, debugging, testing, and deployment systems. The role works directly with Research Engineers to translate research needs into infrastructure requirements and respond quickly to feedback.

Requirements

• Strong software engineering fundamentals and hands-on experience building production-grade LLM inference and training infrastructure, ideally from the ground up.
• Experience with LLM training and inference internals, such as transformers and distributed training, and inference libraries such as vLLM, SGLang, or Megatron.
• Experience with RL training frameworks such as Slime, veRL, Ray Train, SkyRL, or Miles.
• Significant experience and understanding of distributed systems principles.
• Hands-on experience with cloud platforms such as AWS or GCP and container orchestration such as Kubernetes, building systems for high-throughput, low-latency workloads.
• Experience with data engineering tools and building robust, scalable data pipelines.
• Proficiency in core ML frameworks such as PyTorch or JAX.
• Ability to balance production rigor with fast-moving research and communicate infrastructure tradeoffs clearly to researchers who are not infrastructure specialists.

Responsibilities

• Design, build, and scale compute, scheduling, and data infrastructure that powers post-training research on in-house RL environments.
• Develop and maintain core ML framework primitives and internal tools used by researchers, enabling reproducible experimentation and reducing time from idea to result.
• Build evaluation and benchmarking infrastructure, monitoring, logging, and debugging tools, and automated testing and deployment systems.
• Partner directly with Research Engineers to translate research needs into infrastructure requirements and ship quickly in response to feedback.

Benefits

• Competitive cash and equity compensation above the 90th percentile.
• Ownership and autonomy in a fast-moving startup environment.
• Opportunity to work alongside senior and staff engineers from frontier labs and infrastructure companies, as well as top ML engineers.
• Health, vision, and dental benefits.
• 401(k) match.
• Lunch provided every day onsite.
• Weekly snack orders.
• Visa sponsorship and relocation support available.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed