Senior AI Training Systems Architect

Designworks Talent LLC; Bellevue (WA); Hybrid; USD 180,000 - 260,000; Full time

Benefits offered by this job

Medical Insurance

Dental Insurance

Vision Insurance

401(k) with company match

Paid holidays

Job summary

Designworks Talent LLC in Bellevue, WA is seeking AI Training Infrastructure Engineers to build and scale the distributed systems powering next-generation AI model training. Hybrid work from Bellevue with strong ownership in a fast-moving team.

You will work across GPU clusters, model pipelines, and tooling to improve reliability, efficiency, and production readiness for large-scale AI workloads.

Qualifications

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

Responsibilities

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

Skills

Distributed training

GPU clusters

Python/Software development

Distributed systems

Production ML pipelines

Ownership & initiative

Tools

Kubernetes

PyTorch Distributed

DeepSpeed

Ray

Job description

Designworks Talent LLC in Bellevue, WA is seeking AI Training Infrastructure Engineers to build and scale the distributed systems powering next-generation AI model training. Hybrid work from Bellevue with strong ownership in a fast-moving team.

You will work across GPU clusters, model pipelines, and tooling to improve reliability, efficiency, and production readiness for large-scale AI workloads.