Member of Technical Staff, ML Engineer

The role

The ML engineer will turn research code into systems that run at scale and build training and inference infrastructure. They will also build services for researchers to launch runs, route across models, and ship results.

What you will do

• Turn research code into systems with contracts, tests and an on-call owner
• Build services to launch runs, route across models and ship results
• Own training and inference infrastructure including distributed jobs, GPU scheduling, model serving, and the shared platform
• Work in one lane such as distributed training, inference serving, ML platform, or gateway and agent infrastructure

Required qualifications

• Three or more years building and operating ML training or inference infrastructure in production at a company that trains or serves models at meaningful scale
• Experience owning a system through a failure
• Three or more years of software engineering on systems others depend on
• Depth in at least one of the following: Distributed training (PyTorch DDP and FSDP, Ray or comparable, multi-node jobs; checkpointing and resume)
• Depth in at least one of the following: LLM inference serving (vLLM, SGLang, Triton or comparable; batching, KV cache, quantization; an endpoint behind a gateway)
• Depth in at least one of the following: ML platform and MLOps (Kubernetes GPU scheduling and preemption, experiment tracking, model and checkpoint registries, metering)
• Depth in at least one of the following: LLM gateway, agent runtime and evaluation infrastructure (API proxying and provider routing with capture and metering; agent frameworks, sandboxes, memory and t

Nice to have

• ML fluency to debug a training loop, a reward signal or inference-time behavior alongside a researcher
• A second lane from the list provided
• GPU infrastructure, CUDA, or performance engineering for ML workloads
• Cloud infrastructure (GCP, AWS) and infrastructure as code
• Prior work embedded with a research team, turning research code into production systems

Benefits

• Salary
• Benefits
• Meaningful early-stage equity

Before you apply

• Candidates must be authorized to work in the United States
• PSI is not able to sponsor or take over sponsorship of employment visas for this role, now or in the future
• May require that the person in this role be a U.S. person (U.S. citizen or national, lawful permanent resident, or individual granted refugee or asylum status) or be eligible to receive required U.S.

About the company

Physical Superintelligence is a startup building AI systems to discover new physics at scale. The company aims to discover and commercialize transformative physics breakthroughs with artificial superintelligence.