Forward Deployed Engineer - LLM Systems

The most important scientific discoveries of our time won't happen in a traditional lab. We're an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs across materials, energy, and beyond. Backed by world-class investors and growing rapidly, we operate at the pace the frontier requires. Our team brings deep expertise, genuine ownership, and an insatiable drive to push the boundaries of what's scientifically possible. ABOUT THE ROLE You will be a key builder behind the world's first on-prem LLM system for atoms, deploying inference and reinforcement learning systems directly into semiconductor fabs where the science happens. The role splits roughly 80% LLM system development and deployment, and 20% semiconductor customer interaction — translating fab requirements into engineering specs and ensuring our systems meet the realities of production. You will move fluidly between improving LLM systems, managing Kubernetes clusters, and interacting with semiconductor experts — owning deployments end-to-end and serving as the technical face of our system to vendor partners. You will also work closely with LLM systems and modeling experts from OpenAI, Anthorpic, xAI, Google, and other frontier labs. WHAT YOU'LL DO - Deploy and operate inference and reinforcement learning systems on-site at semiconductor partner facilities, from bring-up through ongoing operation - Build and maintain the on-prem LLM platform powering our atomic-scale science workflows, including orchestration, scheduling, observability, and reliability - Develop and extend open-source LLM frameworks (SGLang, vLLM, Megatron, Slime) to meet the performance and integration needs of on-prem deployments - Build custom Kubernetes operators and Slurm integrations to run ML workloads in heterogeneous on-prem environments - Own metrics, dashboards, and alerting in Prometheus, Grafana, and PagerDuty - Partner with semiconductor process engineers to translate fab requirements into engineering specs, with a focus on New Product Introduction (NPI) flows YOU WILL THRIVE IN THIS ROLE IF YOU HAVE EXPERIENCE IN: - Deploying inference and/or RL systems in production, including new-cluster bring-up and integration with existing infrastructure - Kubernetes and/or Slurm — for example, building a custom Kubernetes operator for ML systems, or running large-scale workloads on Slurm - Prometheus, Grafana, and PagerDuty, with a strong grasp of how to set up dashboards and reason about system performance - Hands-on framework-level work with SGLang, vLLM, Megatron, Slime, or other open-source inference and RL engines - Systems engineering fundamentals: Linux, networking, distributed systems, GPU computing, and performance debugging - Direct semiconductor process experience across wafer processing modules (deposition, etch, litho, packaging, metrology), FEOL/MEOL/BEOL integration, NPI spec definition, device performance/yield/reliability, and DOE/SPC/APC, with the ability to engage directly with customers ESPECIALLY STRONG CANDIDATES MAY ALSO HAVE - Deployed ML or LLM systems in air-gapped, on-prem, or otherwise constrained environments - Contributed upstream to open-source inference or training frameworks - Shipped real systems in both software systems and semiconductor process engineering - Worked on RL infrastructure at scale, including rollout systems, training/inference co-location, and reward modeling pipelines - Prior forward-deployed or field engineering experience at a research lab, AI company, or deep tech startup MECHANICS: - Minimum education: bachelor's degree or an equivalent combination of education and training or experience - Location: Our lab is located in Menlo Park and we prefer folks to be located in Menlo Park or San Francisco but can be flexible based on role - Compensation: The annual compensation range for this role - $350,000-$400,000 - Visa sponsorship: Yes, we sponsor visas and will do everything we can to assist in this process wit