Member of Technical Staff, ML Engineer

Physical Superintelligence is a startup focused on building AI systems to discover new physics at scale. They are seeking a Member of Technical Staff, ML Engineer to develop and maintain the training and inference systems essential for scaling their AI research.

Responsibilities:

• Own the training and inference infrastructure that Core AI depends on: distributed training jobs, GPU scheduling, and model-serving systems (vLLM, SGLang, or comparable) for both proprietary models and self-hosted inference
• Build the tools and abstractions AI researchers use to launch training runs, iterate on inference providers, and route workloads across models, so a researcher's time goes into the science instead of the plumbing
• Partner with Engineering on the shared platform: capacity planning, observability, and reliability for GPU and inference infrastructure, so training and serving hold up to the same production bar as everything else we ship
• Debug and harden the training and inference stack under real load. Egress failures, stalled retries, and routing edge cases are your problem to close, not someone else's ticket
• Stay hands-on. You write the code, not just the design doc, and you are the first call when a training job stalls or an inference path breaks