Senior Software Engineer – Cortex LLM Training Platform

Snowflake

. Design and build across the full stack, from public training APIs and SDK through the control plane to the GPU data plane .

Posted 9/17/2026full-timeBellevue • Washington • United StatesSenior💰 $200,000 - $287,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and scaling distributed systems, with a strong foundation in GPU and LLM infrastructure. Proficient in designing fault-tolerant services and optimizing performance for machine learning applications.

Highest-signal resume keywords

Distributed SystemsKubernetesGPU InfrastructureFault ToleranceMachine Learning Systems

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Hard Skills

Production ML SystemsScalable Services DesignDebugging Across LayersReliability EngineeringCost Efficiency Optimization

Tools & Technologies

PyTorchDeepSpeed/FSDPRayCUDA/NCCLVLLM

Certifications & Qualifications

BS in Computer Science

Industry Keywords

Serverless GPU ComputeMulti-Tenant SchedulingCapacity-Aware RoutingEnterprise Scale ComponentsEnd-to-End Performance

Tech Stack

Tools & technologies

Distributed SystemsKubernetesPyTorchRay

About the role

Key responsibilities & impact

Design and build across the full stack, from public training APIs and SDK through the control plane to the GPU data plane
Scale distributed systems that make GPU compute serverless
Build multi-tenant scheduling, placement, and capacity-aware routing across regional GPU pools
Implement fault tolerance across distributed systems
Drive end-to-end performance for training, inference, and RL loops
Keep the data plane responsive under heavy concurrent load and keep GPUs saturated
Partner with Snowflake Research to productionize state-of-the-art training and inference techniques
Build reliable, composable components customers can run at enterprise scale

Requirements

What you’ll need

3+ years of experience building and shipping production ML systems for the Intermediate level; 6+ years for the Senior level
Strong distributed systems and infrastructure foundation
Experience designing scalable, fault-tolerant services and operating them on Kubernetes in production
Familiarity with GPU and LLM infrastructure, including PyTorch, DeepSpeed/FSDP, Ray, CUDA/NCCL, and vLLM
Ability to debug across 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
Hands-on LLM post-training/modeling experience is a bonus
Must be authorized to work in the country to which applying
Must reside within commuting distance of, or be open to relocation to, the designated local Snowflake office