AI Infrastructure Engineer
US$180,000 – US$400,000 / yr base salary
Posted 6 Oct 2026
BranchFactor Summary
The original posting with the fluff stripped out
- Management level
- Individual contributor
- Employment
- On-Site
- Contract
- Full-time
| Base salary | US$180,000 – US$400,000 / yr |
|---|
Who We Are
Percepta’s mission is to transform critical institutions with applied AI. We care that industries that power the world (e.g. healthcare, manufacturing, energy) benefit from frontier technology.
To make that happen, we embed with industry-leading customers to drive AI transformation. We bring together:
-
Forward-deployed expertise in engineering, product, and research
-
Mosaic, our in-house toolkit for rapidly deploying agentic workflows
-
Strategic partnerships with Anthropic, McKinsey, AWS, companies within the General Catalyst portfolio, and more
Our team is a quickly growing group of Applied AI Engineers, Embedded Product Managers and Researchers motivated by diffusing the promise of AI into improvements we can feel in our day to day lives.
Percepta is a direct partnership with General Catalyst, a global transformation and investment company.
About the role
We're hiring an AI Infrastructure Engineer to own the infrastructure, deployment, and operational reliability that powers Percepta's AI systems, including the autonomous agents at the core of what we ship.
Part of the work is hardening what exists: tightening our Terraform footprint, strengthening deployment pipelines, bringing more rigor to how we manage infrastructure across regions and providers. Part of it is building what's missing. And part of it is genuinely new territory, figuring out what SRE means when the systems you're operating make autonomous decisions.
The infrastructure patterns for the agentic systems of the future don't exist yet. You'll help define them.
Why this is different
-
You're deploying autonomous systems. The infrastructure contract changes when your workloads have agency.
-
Observability means understanding why an agent made a decision, not just whether a pod is healthy.
-
The gap between research and production is real here. Our teams move optimization algorithms and AI systems from research environments into production, and you'll be part of that handoff. MLOps experience isn't required, but you'll be closer to that boundary than most infra roles.
-
Small team. Real ownership. You're making foundational decisions, not inheriting someone else's.