YOU WILL
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.
QUALIFICATIONS
3 + years (Intermediate) | 6+ years (Senior) 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).
(Bonus) Hands-on LLM post-training / modeling experience — the strongest candidates pair deep infra skills with real post-training intuition.
Snowflake is growing fast, and we’re scaling our team to help enable and accelerate our growth. We are looking for people who share our values, challenge ordinary thinking, and push the pace of innovation while building a future for themselves and Snowflake.
How do you want to make your impact?
For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: careers.snowflake.com http://careers.snowflake.com
