AI Training Infrastructure Engineer

Designworks Talent•Bellevue, WA

26d•Hybrid

About The Position

A well-funded, rapidly growing AI infrastructure company is building a next-generation cloud platform designed to power the full lifecycle of artificial intelligence. The organization is developing a comprehensive AI infrastructure, platform, and services portfolio that supports the full spectrum of AI workloads—including large-scale compute, model training, fine-tuning, inference, and emerging agentic AI applications. Backed by significant long-term investment, the company combines the speed, ownership, and innovation of a startup with the stability and resources of an established parent organization. Engineering teams are intentionally lean, highly collaborative, and AI-native, leveraging modern tooling and automation to build infrastructure capable of supporting the industry's most demanding AI workloads. We're seeking AI Training Infrastructure Engineers to build and scale the distributed systems that power large-scale AI model training. This team focuses on reliability, efficiency, and operational excellence across GPU clusters, enabling researchers and engineers to train and deploy advanced AI models at scale. This is a foundational engineering role focused on building the infrastructure layer behind large-scale AI training workloads. You'll work on distributed training systems, GPU clusters, model pipelines, and the tooling required to make AI development more reliable, efficient, and scalable. You'll collaborate closely with infrastructure, orchestration, performance, and machine learning teams to solve complex challenges around distributed computing, fault tolerance, training efficiency, and production readiness. This opportunity is ideal for engineers who enjoy building highly scalable systems and working 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.
Comfortable operating with high ownership and limited process overhead.

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 impacting 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.