Senior AI Training Infrastructure Engineer | Bellevue (Hybrid)

Designworks Talent; Bellevue (WA); Hybrid; USD 150,000 - 210,000; Full time

Benefits offered by this job

Medical, dental, and vision insurance

401(k) plan with company match

Paid holidays

Job summary

Designworks Talent in Bellevue, WA is seeking an AI Training Infrastructure Engineer to build and scale the distributed training stack in a hybrid role. You will work on large GPU clusters and production-ready pipelines that power state-of-the-art AI training workloads.

You will collaborate with platform, orchestration, performance, and ML teams to tackle scaling, reliability, and efficiency challenges while shaping tooling and best practices.

Qualifications

• Hands-on experience building and operating distributed training systems.
• Experience supporting large AI models or ML workflows.
• Ability to own technically challenging projects with limited process overhead.

Responsibilities

• Build and scale distributed training infrastructure for large AI models across GPU clusters.
• Improve training reliability, efficiency, and resource utilization.
• Develop fault tolerance, checkpointing, recovery for large-scale training.
• Integrate training systems with production ML pipelines.
• Diagnose throughput, stability, reliability, and cost issues.
• Create tools and automation to improve researcher/developer experience.
• Establish best practices for training infra and platform reliability.
• Contribute to evolution of the AI infra platform as an early engineer.

Skills

Distributed training systems

Multi-node GPU training

Production ML pipelines

Kubernetes

Ray / PyTorch Distributed

DeepSpeed / Megatron-LM

Ownership in fast-moving env

Tools

Kubernetes

PyTorch Distributed

DeepSpeed

Ray

Megatron-LM

Job description

Designworks Talent in Bellevue, WA is seeking an AI Training Infrastructure Engineer to build and scale the distributed training stack in a hybrid role. You will work on large GPU clusters and production-ready pipelines that power state-of-the-art AI training workloads.

You will collaborate with platform, orchestration, performance, and ML teams to tackle scaling, reliability, and efficiency challenges while shaping tooling and best practices.