Member of Technical Staff, ML Engineer

📋 Description

• 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.

🎯 Requirements

• 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.
• Hands-on experience with distributed training (multi-GPU or multi-node, using PyTorch, Ray, or comparable) and model-serving systems (vLLM, SGLang, Triton, or comparable).
• Strong software engineering fundamentals. You can build a service that other engineers and researchers depend on every day, not a script that worked once.
• Enough ML fluency to work productively with AI researchers: you understand training loops, reward signals, and inference-time behavior well enough to debug them, even without designing the algorithms yourself.
• Experience building internal platform tools such as training-as-a-service APIs, inference gateways, or job schedulers.
• Background in GPU infrastructure, CUDA, or performance engineering for ML workloads.

🎁 Benefits

• Competitive compensation and meaningful equity
• Remote/hybrid consideration on a case-by-case basis
• Opportunity to work with leading AI researchers on discovery