Infrastructure Engineer

Oho Group; San Francisco (CA); On-site; USD 150,000 - 190,000; Full time

Job summary

Oho Group in San Francisco is seeking an AI Infrastructure Engineer — Hardware Systems to build robust environments for hardware workflows and AI agent integration.

You will design containerized and virtualized runtimes, develop tooling for researcher productivity, and work at the intersection of AI research, software infrastructure and semiconductor engineering.

The role offers ownership of infrastructure while staying close to cutting-edge hardware design and ML workflows.

Qualifications

• 2–8 years of software or infrastructure engineering experience.
• Strong Python and practical experience with Docker, containers and VMs.
• Exposure to digital design, VLSI, FPGA, verification, EDA or semiconductor tooling.
• Working knowledge of machine-learning concepts.
• Experience with reinforcement learning, synthetic task generation, agent evaluation, developer productivity or post-training data would be particularly useful.

Responsibilities

• Build containerized and virtualized environments for hardware workflows.
• Create infrastructure for running and evaluating AI agents at scale.
• Integrate engineering tools into reliable, reproducible execution environments.
• Develop internal tooling that improves researcher and engineer productivity.
• Support data generation, filtering and evaluation workflows.
• Build observability, testing and failure-recovery into agent infrastructure.
• Work across AI research, software infrastructure and semiconductor engineering.

Skills

Python programming

AI coding agents usage

Machine learning concepts

Adaptability

Tools

Docker

Containers

Virtual machines

Job description

AI Infrastructure Engineer — Hardware Systems

We’re working with an early-stage AI company applying advanced AI agents to semiconductor engineering.

They’re hiring an infrastructure engineer who combines strong software and platform fundamentals with enough hardware knowledge to understand chip-design workloads. You’ll build the environments, tooling and data infrastructure that allow AI agents to interact reliably with complex engineering tools.

What you’ll work on

• Build containerized and virtualized environments for hardware workflows
• Create infrastructure for running and evaluating AI agents at scale
• Integrate engineering tools into reliable, reproducible execution environments
• Develop internal tooling that improves researcher and engineer productivity
• Support data generation, filtering and evaluation workflows
• Build observability, testing and failure-recovery into agent infrastructure
• Work across AI research, software infrastructure and semiconductor engineering

What we’re looking for

• 2–8 years of software or infrastructure engineering experience
• Strong Python and practical experience with Docker, containers and VMs
• Regular use of AI coding agents such as Claude Code or Cursor
• Exposure to digital design, VLSI, FPGA, verification, EDA or semiconductor tooling
• Working knowledge of machine-learning concepts
• Adaptability and comfort building without mature internal platforms

Experience with reinforcement learning, synthetic task generation, agent evaluation, developer productivity or post-training data would be particularly useful.

This role suits someone who wants infrastructure ownership but also wants to work much closer to AI research and hardware engineering than a conventional platform position allows.