AI Training Infrastructure - Member of Technical Staff

Designworks Talent • Bellevue, WA

1d • Hybrid

About The Position

This foundational engineering role builds the infrastructure layer behind large-scale AI training workloads. The team develops distributed training systems, GPU clusters, model pipelines, and tools that make AI development more reliable, efficient, and scalable. The role collaborates with infrastructure, orchestration, performance, and machine learning teams on distributed computing, fault tolerance, training efficiency, and production readiness. It is suited to engineers who enjoy building highly scalable systems at the intersection of AI research, infrastructure engineering, and distributed computing.

Requirements

• Hands-on experience building and operating distributed training systems or large-scale machine learning infrastructure.
• Experience supporting large AI models, foundation models, post-training workflows, or similar ML systems.
• Strong understanding of the reliability, scalability, and efficiency challenges associated with multi-node GPU training.
• Experience integrating training systems with production machine learning pipelines.
• Strong programming skills and experience working with complex distributed systems.
• Ability to independently own technically challenging projects in a fast-moving engineering environment.
• Comfort operating with high ownership and limited process overhead.
• U.S. work authorization is required; visa sponsorship is not currently available.

Nice To Haves

• Experience with distributed training frameworks such as PyTorch Distributed, DeepSpeed, Megatron-LM, Ray, or similar technologies.
• Experience with supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), or other post-training workflows.
• Background operating AI training infrastructure at scale within a hyperscaler, AI research organization, cloud provider, or GPU cloud environment.
• Experience optimizing GPU utilization, training performance, or distributed system reliability.
• Familiarity with Kubernetes, containerized AI workloads, and large-scale infrastructure platforms.

Responsibilities

• Build and scale distributed training infrastructure supporting large AI models across large GPU clusters.
• Design and improve systems that increase training reliability, efficiency, and resource utilization.
• Develop solutions for fault tolerance, checkpointing, recovery, and large-scale training operations.
• Integrate AI models into production training pipelines in partnership with platform, orchestration, and performance engineering teams.
• Diagnose and resolve issues affecting training throughput, stability, reliability, and cost efficiency.
• Build tools and automation that improve the developer experience for AI researchers and engineers.
• Establish best practices for training infrastructure, operational processes, and platform reliability.
• Contribute to the evolution of the AI infrastructure platform as an early member of the engineering team.

Benefits

• Medical insurance.
• Dental insurance.
• Vision insurance.
• 401(k) plan with company match.
• Paid holidays.
• Certain roles may be eligible for merit increases, annual bonus, and long-term incentives based on individual performance.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed