Member of Technical Staff - AI/ML Infrastructure Engineer, RL Post-training
Preference Model· Engineering
San Francisco$200k - $350k + equityFull-timePosted yesterday
About the role
Senior ML Infrastructure Engineer role focused on building scalable infrastructure to power frontier post-training research on large language models, RL environments, and in-house RL post-training workflows.
Responsibilities
- 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
Qualifications
- 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, Megatron
- Experience working on RL training frameworks like Slime, veRL, Ray Train, SkyRL, Miles
- Significant experience and understanding of distributed systems principles, and 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
- Proficiency in core ML frameworks such as PyTorch or JAX
- Can balance production rigor with the pace of fast-moving research, and communicate infrastructure tradeoffs clearly to researchers who aren't infra specialists
Skills
Required:Software engineering fundamentalsLLM training/inference infrastructureDistributed systemsPyTorch or JAX
Nice to have:Data engineering / pipelines
Benefits
- 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
Last updated Oct 25, 2026. Interested? Apply on the company site.
About Preference Model
Preference Model is building the next generation of training data to power the future of AI.
Automated ML research engineeringLLMsRL environmentsbessemer-ai-infrastructure-2026frontier AIreinforcement-learning-platformstraining data
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