Member of Technical Staff - Machine Learning Infrastructure Engineer, Post-training

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.

Frontier research moves only as fast as its infrastructure permits. Building solid infrastructure is foundational to our mission of pushing self-directed learning as far as it can go.

We are looking for Senior
ML Infrastructure Engineers to build the systems that power the frontier of post-training on large language models
. This role involves building scalable infrastructure to enable high-throughput systems and shape how our research is run, bringing us closer to models that can train themselves on what they aren't yet good at.

• Design, build, and scale the compute, scheduling, and data infrastructure that powers post-training research on our in-house RL environments
• Develop and maintain core ML framework primitives and internal tooling that researchers rely on daily, accelerating reproducible experimentation and reducing time from idea to result
• Build evaluation and benchmarking infrastructure, monitoring, logging, and debugging tooling, and automated testing and deployment systems, so failures are caught early and infrastructure stays reliable as it scales
• Partner directly with Research Engineers to translate research needs into infrastructure requirements, and ship fast in response to their feedback

• Have strong software engineering fundamentals, experience building production-grade infrastructure (ideally for ML or data-intensive systems), and proficiency in core ML frameworks such as PyTorch or JAX
• Significant experience and understanding of distributed systems principles, and have hands-on experience with cloud platforms (AWS, GCP) and container orchestration (Kubernetes), building systems for high-throughput, low-latency workloads
• Have experience with data engineering tools and building robust, scalable data pipelines
• Experience working on RL training frameworks like Slime, veRL, Ray
• Have some familiarity with LLM training/inference internals (transformers, distributed training, inference libraries like vLLM or SGLang); deep expertise is a plus, not a requirement
• Can balance production rigor with the pace of fast-moving research, and communicate infrastructure tradeoffs clearly to researchers who aren't infra specialists

• Competitive cash and equity compensation (>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, plus top ML 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.