Senior Software Engineer — LLM Post-Training Platform

Responsibilities

• Design and build across the full stack — from the public training APIs and SDK through the control plane to the GPU data plane.
• Scale the distributed systems that make GPU compute serverless — multi-tenant scheduling, placement, and capacity-aware routing across regional GPU pools, with fault tolerance built in.
• Drive end-to-end performance at scale — keep the training, inference, and RL loops fast and the data plane responsive under heavy concurrent load, with GPUs kept saturated.
• Productionize research building blocks — partner with Snowflake Research to turn state-of-the-art training and inference techniques into reliable, composable components customers can run at enterprise scale.

Requirements

• 5+ years building and shipping production ML systems
• Strong distributed systems and infrastructure foundation — designing scalable, fault-tolerant services and operating them on Kubernetes in production
• Familiarity with GPU and LLM infrastructure — e.g., PyTorch, DeepSpeed/FSDP, Ray, CUDA/NCCL, vLLM; able to debug across the data, infrastructure, and GPU layers
• Demonstrated ability to harden complex systems for reliability, throughput, and cost efficiency
• BS in Computer Science or a related field (MS/PhD a plus)

Nice-to-Haves

• Hands-on LLM post-training / modeling experience — the strongest candidates pair deep infra skills with real post-training intuition