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Preference Model is a superintelligence data research company building RL environments for training capable, better-aligned large language models. The company has raised $16M in seed funding led by a16z and counts Anthropic data team veterans on its founding team.
As a Senior ML Infrastructure Engineer, you will design, build, and scale the compute, scheduling, and data infrastructure powering post-training research on the company's in-house RL environments. You'll develop and maintain core ML framework primitives and internal tooling that research engineers depend on daily, accelerating reproducible experimentation and reducing iteration time. You'll also build evaluation and benchmarking infrastructure, monitoring, logging, debugging tools, and automated testing and deployment systems to catch failures early and maintain reliability at scale.
You'll partner directly with Research Engineers to translate research needs into infrastructure requirements and ship fast in response to feedback. This is a hands-on role where you'll work on the systems that enable frontier-level post-training research.
The company offers competitive cash and equity compensation (>90th percentile), ownership and autonomy in a fast-moving startup, the opportunity to work alongside senior and staff engineers from frontier labs and infrastructure companies, health/vision/dental benefits, 401K match, daily onsite lunch, weekly snack orders, and visa sponsorship and relocation support.
REQUIREMENTS:
- Strong software engineering fundamentals and hands-on experience building production-grade LLM inference and training infrastructure, ideally from the ground up
- Experience building LLM training/inference internals such as transformers, distributed training, and working on inference libraries like vLLM, SGLang, or Megatron
- Experience working on RL training frameworks such as Slime, veRL, Ray Train, SkyRL, or Miles
- Significant experience and understanding of distributed systems principles, with hands-on experience on cloud platforms (AWS, GCP) and container orchestration (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 the pace of fast-moving research, and communicate infrastructure tradeoffs clearly to researchers who aren't infrastructure specialists
