Infrastructure Engineer

Oho Group; San Francisco (CA); On-site; USD 140,000 - 200,000; Full time

Job summary

Oho Group is a fast-scaling startup in San Francisco, building the world’s first autonomous chip-design platform. You will help scale internal infrastructure that powers AI-driven design and testing workflows, enabling agents to run complex digital-design, FPGA and EDA tasks.

You will create reliable, isolated environments, integrate traditional semiconductor tooling with modern AI infrastructure, and support post-training workflows from data selection to agent benchmarks, in a dynamic

Qualifications

• Strong infrastructure fundamentals with Python, Docker, containerisation and virtual machines.
• Experience building internal platforms or tooling for engineering teams.
• Familiarity with AI coding agents and tools such as Claude Code or Cursor.
• Understanding of AI/ML systems; reinforcement learning exposure valuable.
• Pragmatic, adaptable mindset and willingness to take ownership across multiple areas in an early-stage environment.

Responsibilities

• Build and scale the internal infrastructure that enables AI agents to run complex digital-design, FPGA and EDA workflows using Python, Docker and virtual machines.
• Develop reliable, isolated environments for executing, monitoring and debugging demanding engineering workloads.
• Bridge traditional semiconductor tooling with modern AI infrastructure in a fast-moving startup environment.
• Support post-training workflows spanning data selection, filtering, evaluation environments and agent benchmarks.

Skills

Python

Docker

Containerization

Virtual Machines

AI agents

Reinforcement Learning

Ownership & adaptability

Tools

Claude Code

Cursor

Job description

Join a fast-scaling startup building the world’s first autonomous chip-design platform, already working with a leading frontier-model lab.

• Build and scale the internal infrastructure that enables AI agents to run complex digital-design, FPGA and EDA workflows using Python, Docker and virtual machines.
• Develop reliable, isolated environments for executing, monitoring and debugging demanding engineering workloads.
• Bridge traditional semiconductor tooling with modern AI infrastructure in a fast-moving startup environment.
• Support post-training workflows spanning data selection, filtering, evaluation environments and agent benchmarks.

Key requirements

• Strong infrastructure fundamentals, including Python, Docker, containerisation and virtual machines.
• Experience building internal platforms, developer infrastructure or tooling for complex engineering teams.
• Familiarity with AI coding agents and tools such as Claude Code or Cursor.
• An understanding of AI/ML systems; exposure to reinforcement learning or post-training workflows would be valuable.
• A pragmatic, adaptable mindset and willingness to take ownership across multiple areas in an early-stage environment.