AI Training Infrastructure, Technical Staff

Designworks Talent LLC

. Build and scale distributed training infrastructure supporting large AI models across large GPU clusters .

Posted 11/10/2026full-timeBellevue • Washington • United StatesLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and scaling distributed training infrastructure for large AI models, with a strong focus on reliability, efficiency, and resource utilization. Proficient in integrating AI models into production pipelines and optimizing GPU performance within large-scale environments.

Highest-signal resume keywords

Distributed Training SystemsLarge-Scale Machine Learning InfrastructurePyTorch DistributedKubernetesGPU Utilization Optimization

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Distributed Training FrameworksSupervised Fine-TuningReinforcement Learning From Human FeedbackComplex Distributed Systems ProgrammingFault Tolerance Solutions

Soft Skills

Project OwnershipProblem Solving

Tools & Technologies

DeepSpeedMegatron-LMRayContainerized AI Workloads

Industry Keywords

AI ModelsTraining InfrastructureOperational ProcessesPerformance EngineeringHyperscaler

Tech Stack

Tools & technologies

CloudDistributed SystemsKubernetesNode.jsPyTorchRay

About the role

Key responsibilities & impact

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 with platform, orchestration, and performance engineering teams
Diagnose and resolve issues affecting training throughput, stability, reliability, and cost efficiency
Build tools and automation to 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 engineering team member

Requirements

What you’ll need

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 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
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
U.S. work authorization is required

Benefits

Comp & perks

Certain roles are eligible for merit increases, annual bonus, and long term incentives
Medical, dental, and vision insurance for U.S.-based employees
401(k) plan and company match
Paid holidays per calendar year
Hybrid work arrangement