Skip to main content

Human Computer Lab

Intern - Software/ML Engineer

San Francisco (Remote)interninternAdded 1 month ago

About this role

Join this robotics startup to develop software and machine learning systems powering interactive consumer robots. You'll work on perception, intelligence, and behavior systems—from ML model development to hardware integration—helping transform research into production-ready robotics.

What you'll do

  • Develop software systems for robot perception, intelligence, and behavioral control
  • Build and train ML models for computer vision, audio processing, and interaction understanding
  • Design pipelines for training and deploying machine learning models to production
  • Integrate ML systems with robotic hardware and embedded systems
  • Collaborate with robotics engineers to create integrated robotic solutions
  • Improve robot perception and responsiveness through iterative development

What they're looking for

  • Python and/or C++ programming
  • Machine learning frameworks and model development
  • Computer vision or robotics experience
  • Software engineering fundamentals and algorithms
  • ML simulation tools (Isaac Sim, MuJoCo, etc.)
  • Reinforcement learning or multimodal AI
  • System integration and debugging
  • Problem-solving and creative thinking
Apply with Autofill

Opens the application — the Jobs AI extension fills it for you. Set up autofill

Opens the official application on the employer’s site. No login required.

Human Computer Lab

Human Computer Lab is a robotics startup building LeLamp, an expressive consumer robot designed to be responsive and lifelike. The company is hiring ML engineers, controls engineers, and electrical engineers to develop the intelligence systems, motion control, and hardware that bring the robot to life from research through production deployment.

View all jobs at Human Computer Lab

Likely interview questions

  • Tell us about your experience with machine learning frameworks and computer vision. How have you applied them to a specific project?
  • Describe your experience integrating ML models with hardware or embedded systems. What challenges did you face and how did you solve them?