AI Infrastructure Engineer

Fuel Talent; Seattle (WA); On-site; USD 180,000 - 210,000; Full time

Job summary

Fuel Talent in Seattle is seeking an AI Infrastructure Engineer to lead the 0-to-1 build of the core compute and orchestration layer for ML workloads. You will own GPU orchestration and resource management, shaping engineering standards from day one.

You will design researcher-friendly SDKs/APIs, operationalize ML pipelines, and partner with customers to improve model performance and observability, while guiding the tech roadmap and security posture.

Qualifications

• 5+ years of software engineering experience focused on ML infrastructure or backend systems.
• Proficiency in Golang, Python, or Typescript.
• Deep Kubernetes experience for GPU workloads on AWS/GCP.
• Experience deploying ML training or inference pipelines in production (PyTorch, Hugging Face).

Responsibilities

• Architect the GPU compute fabric and orchestration layer for ML workloads.
• Design SDKs and APIs to streamline ML workflows for researchers.
• Operationalize end-to-end ML pipelines from data ingestion to model serving.
• Collaborate with customers to debug jobs and build observability tools.
• Lead the technical roadmap and influence infra and security decisions.

Skills

ML infra

Golang

Python

Typescript

Kubernetes

System design

Tools

AWS

GCP

PyTorch

Hugging Face

Job description

Compensation: $180K–210K + 0.1%–0.5% equity

Visa Support: NO; due to the sensitive nature of this role, we are only able to accommodate those with US Citizenship or Green Card Holder status

We are partnered with a seed-stage startup building a platform for scientific ML.

As an AI Infrastructure Engineer, you will report to the CTO and lead the 0-to-1 build of the core compute and orchestration layer, holding significant ownership and shaping the technical direction from day one.

What you'll do

• Architect the GPU compute fabric: build and manage the orchestration layer for GPU workloads, ensuring efficient resource allocation and cost management across training, fine-tuning, and inference
• Design developer-centric SDKs and APIs that turn complex ML workflows into intuitive experiences for researchers and data scientists
• Operationalize the ML lifecycle with robust end-to-end pipelines, from data ingestion and preprocessing to secure model serving and monitoring
• Partner with customers to debug fine-tuning jobs and build observability tools for tracking model performance and resource health in real time
• Lead the technical roadmap, making critical build-vs-buy decisions on infrastructure and security while shaping engineering standards and hiring

What we're looking for

• 5+ years of software engineering experience, focused on ML infrastructure or backend systems supporting ML workloads
• Programming expertise in Golang, Python, or Typescript
• Deep Kubernetes experience, ideally for GPU workloads on AWS/GCP
• Experience deploying and operating ML/DL training or inference pipelines in production (PyTorch, Hugging Face, or similar)
• Strong CS fundamentals and system design skills
• Ability to thrive in fast-paced, ambiguous environments