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Cobot · 3 weeks ago

SWE Intern, AI, Navigation, and Controls Intern (Master’s / PhD)

Santa Clara, CA

Internship

Onsite

Intern

$40/hr - $45/hr

Start in 2026 Summer

Cobot is a growing robotics startup focused on advancing autonomous robots through innovative AI and machine learning solutions. The SWE Intern will develop software for the on-robot autonomy stack, applying cutting-edge AI research to enhance robot performance in dynamic environments.

Information TechnologyMachinery ManufacturingRobotics

No H1B

Responsibilities

Develop and maintain software for our on-robot autonomy stack leveraging the latest AI research in navigation, controls, multimodal models, end-to-end models, and LLMs

Leverage open source models and the latest research papers to improve the capabilities of our robots operating in customer sites

Train machine learning models for navigation, controls, and behaviors on our robot

Optimize trained models for on-robot deployment

Qualification

Machine LearningDeep LearningPythonPytorchTensorflowMultimodal ModelsGitLinuxCommunication SkillsTeamworkAdaptability

Required

Pursuing a graduate degree in Computer Science, Robotics, AI, Machine Learning, or related field

Experience with deep learning frameworks such as Pytorch, Tensorflow, etc

Understanding of multimodal models, modern ML architectures and approaches (transformers, diffusion models, reinforcement learning, etc.)

Proficiency with Git, version control, and Linux environments

Skilled in Python

Highly motivated teammate with excellent oral and written communication skills

Enjoy working in a fast paced, collaborative and dynamic start-up environment as part of a small team

Willing to occasionally travel

Must have and maintain US work authorization

Preferred

Demonstrated ability to deploy ML models to robots in production

Experience with end to end learning approaches for navigation and controls

Experience with perception for robotics, autonomous vehicles, or other real world applications

Practical experience with ML software engineering best practices – for example, experiment tooling, data pipelines, model training, and workflow automation

Experience optimizing ML models for deployment in compute constrained environments

Familiarity with edge compute architectures