Senior Software Engineer — LLM Post-Training Platform
Build and scale Snowflake's Cortex Training LLM post-training platform, handling distributed GPU scheduling, orchestration, and productionizing research for enterprise-scale model adaptation.
About the job
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
Skills
PyTorch, Deepspeed, Fsdp, Ray, CUDA, Nccl, vLLM, Kubernetes, Distributed Systems, Llm Post-Training
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