Vehicle Platform Infrastructure Engineer — Equity Options

Humble Robotics; San Francisco, Northern (CA, KY); Hybrid; USD 140,000 - 200,000; Full time

Job summary

Humble Robotics in San Francisco, CA is seeking an infrastructure engineer to design, build, and scale systems that connect our autonomous vehicles to our platform. You will own data offload pipelines, integrate software with compute and sensors, and influence end-to-end infrastructure decisions to enable fast model iteration.

The ideal candidate is a broad generalist who can work across stack levels, contribute to security- and performance-focused design, and thrive in a small, fast-moving team.

Qualifications

• Experience with infrastructure-as-code and configuration management tools (Terraform, Ansible).
• Expertise building and maintaining CI/CD pipelines and managing deployments.
• Expertise in OS, networking, and security fundamentals including Linux administration, system and application performance tuning, enterprise networking, and cryptography in practice.
• Comfortable reading, writing, and extending non-trivial code within a production robotics/AV codebase.

Responsibilities

• Build the data collection networking and infrastructure that offloads large volumes of sensor data from vehicles into the training platform.
• Collaborate with onboard and hardware teams to integrate software with compute, sensor, and navigation systems on autonomous vehicles.
• Translate workflows into reliable infrastructure; help teams deploy software and models into the real world.

Skills

IaC experience

CI/CD expertise

Linux administration

Networking basics

Cryptography basics

Production robotics code

Tools

Terraform

Ansible

CI/CD pipelines

Job description

Humble Robotics in San Francisco, CA is seeking an infrastructure engineer to design, build, and scale systems that connect our autonomous vehicles to our platform. You will own data offload pipelines, integrate software with compute and sensors, and influence end-to-end infrastructure decisions to enable fast model iteration.

The ideal candidate is a broad generalist who can work across stack levels, contribute to security- and performance-focused design, and thrive in a small, fast-moving team.