--- title: Member Technical Staff ML Infrastructure Engineer, RL description: Preference Model is hiring for the Member Technical Staff ML Infrastructure Engineer, RL Post Training role. See the full description and apply. type: job url: https://www.foundrole.com/jobs/member-technical-staff-ai-ml-infrastructure-engineer-rl-post-training-at-preference-model-01a158d0-ce84-7b84-a360-4b6bb3c99358 date: 2026-11-15T01:26:44Z og_description: Join Preference Model as Member Technical Staff ML Infrastructure Engineer, RL Post Training in San Francisco, CA. og_image: https://www.foundrole.com/og/u1nc1e.png breadcrumbs: - label: Home url: https://www.foundrole.com/ - label: Search url: https://www.foundrole.com/jobs --- | | | |---|---| | **Company** | Preference Model | | **Location** | San Francisco, CA | | **Salary** | $200K/yr - $350K/yr | | **Type** | Full Time | | **Posted** | Oct 20, 2026 | | **History** | First seen 25 days ago (2026-10-20), reposted 2 times | ## Is this job real? **Ghost job check:** A · Low risk. Probably a real opening. ## Visa sponsorship - **H-1B:** Preference Model sponsors H-1B visas. It filed 1 H-1B petition (FY2026 Q1 – FY2026 Q4). - **E-Verify:** No E-Verify enrollment on record for Preference Model. ## Pay for similar jobs Most similar jobs pay $213,000 – $351,000 a year (median $268,000). ## 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. We're trying to address both of these issues in the most direct, highest leverage way available to us, which today means researching how to build RL environments better, not just for the AIs of today but for the AIs as they will be when they outsmart us. Over the past year, we've built RL environments for several frontier labs, and we're backed by $16M in seed funding led by a16z. Our founding team has previous experience on Anthropic’s data team building data infrastructure, and datasets behind Claude. # **About the Role** 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 infrastructure and 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.   # **What You Will Do** - 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 # **What We are Looking For** - 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 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 - 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 # **What We Offer:** - 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.** ## Skills - Cloud Computing - Balance Sheet - Debugging - Amazon Web Services - Machine Learning Model Training - Building Management Systems (Bms) - High-Throughput Screening - Software Deployment - Container Orchestration - Logging - CaseWare International IDEA - Megatron-LM - LLM Inference - SGLang - Kubernetes - Low Latency - IT Infrastructure - Benchmarking - Distributed Training (Machine Learning) - Reinforcement Learning from Human Feedback (Rlhf) - ML Training Infrastructure - Automated Testing - JAX - Large Language Models (Llm) - Google Cloud Platform (Gcp) - Reinforcement Learning - Data Engineering - Machine Learning - Electrical Machines - Ray (Distributed Computing) - vLLM - Data Pipeline - Distributed Systems ## Benefits - Health Insurance - Dental Insurance - Vision Insurance - 401(k) Plans - Employee Stock Options (ESOs) - Company Events ## Related - [Data & Analytics jobs](https://www.foundrole.com/careers/data-and-analytics?utm_source=ai_markdown)- [Machine Learning & AI jobs](https://www.foundrole.com/careers/data-and-analytics/machine-learning-and-ai?utm_source=ai_markdown)- [Artificial Intelligence Engineer jobs](https://www.foundrole.com/careers/data-and-analytics/machine-learning-and-ai/artificial-intelligence-engineer?utm_source=ai_markdown)- [Machine Learning Engineer jobs](https://www.foundrole.com/careers/data-and-analytics/machine-learning-and-ai/artificial-intelligence-engineer/machine-learning-engineer?utm_source=ai_markdown)- [Browse all companies](https://www.foundrole.com/companies?utm_source=ai_markdown) ## Explore this job market - [California](https://www.foundrole.com/locations/us/california?utm_source=ai_markdown) — 260758 jobs - [San Francisco, CA](https://www.foundrole.com/locations/us/california/san-francisco?utm_source=ai_markdown) — 27903 jobs - [Software Development](https://www.foundrole.com/sectors/technology/software-development?utm_source=ai_markdown) — 14688 jobs ## Related pages - [California](https://www.foundrole.com/locations/us/california?utm_source=ai_markdown) — 260758 jobs - [San Francisco, CA](https://www.foundrole.com/locations/us/california/san-francisco?utm_source=ai_markdown) — 27903 jobs - [Software Development](https://www.foundrole.com/sectors/technology/software-development?utm_source=ai_markdown) — 14688 jobs ## Browse - [Companies](https://www.foundrole.com/companies?utm_source=ai_markdown) - [Jobs by country](https://www.foundrole.com/locations?utm_source=ai_markdown) - [Jobs in the United States by state and city](https://www.foundrole.com/locations/us?utm_source=ai_markdown) - [H1B sponsors by location](https://www.foundrole.com/h1b-sponsors?utm_source=ai_markdown) - [H1B salaries](https://www.foundrole.com/h1b-salaries?utm_source=ai_markdown) - [Sectors and industries](https://www.foundrole.com/sectors?utm_source=ai_markdown) - [Jobs by career field](https://www.foundrole.com/careers?utm_source=ai_markdown) - [Latest jobs](https://www.foundrole.com/jobs/latest?utm_source=ai_markdown)