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Internship Machine Learning Robotics Jobs in California

Horizon Robotics

Research Scientist

Cupertino, CA · Hybrid

$150K - $300K/yr

We are looking for someone with expertise in and enthusiasm for machine learning research, especially in Robotics, Embodied AI, Reinforcement learning (RL) , etc. As a Research Scientist in the team ...

Horizon Robotics

Research Scientist

Cupertino, CA · On-site

$150K - $300K/yr

We are looking for someone with expertise in and enthusiasm for machine learning research, especially in Robotics, Embodied AI, Reinforcement learning (RL) , etc. As a Research Scientist in the team ...

Applied Intution

... moving machine on the planet. Applied Intuition services the automotive, defense, trucking ... We are looking for multiple passionate Research Interns to join the Research Group at Applied ...

Horizon Robotics

Research Scientist

Cupertino, CA · Hybrid

$150K - $300K/yr

We are looking for someone with expertise in and enthusiasm for machine learning research, especially in Robotics, Embodied AI, Reinforcement learning (RL) , etc. As a Research Scientist in the team ...

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Internship Machine Learning Robotics information

What is the difference between Internship Machine Learning Robotics vs Internship Data Science?

AspectInternship Machine Learning RoboticsInternship Data Science
Required SkillsProgramming (Python, C++), Robotics, Machine LearningStatistics, Programming (Python, R), Data Analysis
Work EnvironmentRobotics labs, manufacturing, research facilitiesData centers, corporate offices, research institutions
Industry UsageRobotics companies, automation, AI hardwareFinance, healthcare, marketing, tech firms

Internship Machine Learning Robotics focuses on developing AI algorithms for robotic systems, combining hardware and software skills. In contrast, Internship Data Science emphasizes analyzing data to extract insights, often in business or research settings. Both internships require programming skills, but their applications and environments differ significantly.

How to become an internship machine learning robotics?

To become an intern in machine learning robotics, candidates typically need a background in computer science, robotics, or related fields, along with programming skills in languages like Python or C++. Gaining experience with machine learning frameworks such as TensorFlow or PyTorch and understanding robotics platforms like ROS can be beneficial. Applying to internships through university programs, online job portals, or company websites and demonstrating relevant coursework, projects, or certifications can improve chances of selection.

What are the most commonly searched types of Machine Learning Robotics jobs in California?

The most popular types of Machine Learning Robotics jobs in California are:

What cities in California are hiring for Internship Machine Learning Robotics jobs?

Cities in California with the most Internship Machine Learning Robotics job openings:

Internship Machine Learning Robotics jobs near you
Infographic showing various Internship Machine Learning Robotics job openings in California as of July 2026, with employment types broken down into 1% As Needed, 76% Full Time, 21% Part Time, 1% Temporary, and 1% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution.

Machine Learning Engineer - Robot Manipulation

San Francisco, CA • On-site

Maven Robotics
See more jobs at Maven Roboticsexternal

Full-time

Re-posted 14 days ago

Job description

Company Overview
Maven Robotics is building the world's leading general-purpose AI robots.
We are currently operating in stealth and are growing the world's best team in AI robotics. We are looking for self-starters that are the world's best in their field, who can innovate from a deep understanding of the fundamentals, and who share our values of unwavering truth seeking and integrity, humility, curiosity, and relentless determination.
Role Description
We are looking to recruit an exceptional Machine Learning Engineer - Robot Manipulation to design, implement, test, and deploy robot manipulation algorithms that enable assembly and material movement tasks.
In this role you will:
  • Design and implement machine learning algorithms, with a focus on reinforcement learning (RL) and imitation learning (IL), to enable robotic manipulators to perform complex tasks in dynamic environments.
  • Translate high-level objectives into machine learning problems and deploy robust, scalable models to real-world robotic systems.
  • Integrate your ML solutions into existing robotics workflows, ensuring that models are performant in both simulated and real-world settings.
  • Drive innovation by incorporating the latest research in machine learning into practical applications that push the boundaries of robotic manipulation.
  • Take ownership of critical ML projects, seeing them through from conception to successful deployment.
  • Collaborate across disciplines to ensure seamless integration of ML models and provide technical mentorship to junior engineers.
Qualifications
Must-have:
  • MS or PhD in machine learning, computer science, robotics, or a related field.
  • Strong practical experience in training and deploying machine learning models for real-world applications.
  • Deep understanding of reinforcement learning (RL) and imitation learning (IL) and their application to robotics.
  • Proficiency in programming languages and tools commonly used in machine learning (e.g., Python, PyTorch).
  • Experience with data collection, preprocessing, and management in the context of training ML models.
  • Self-starter attitude with strong ability to identify problems, prioritize them, then plan and execute working solutions.
  • Enthusiasm for working in a fast paced startup environment and eagerness to support the team on a variety of topics.

Nice-to-have:
  • Familiarity with robotic simulation environments (e.g., Gazebo, MuJoCo) and experience in sim-to-real transfer.
  • Experience in:
    • Designing and implementing reward functions for complex manipulation tasks.
    • Developing models that can handle noisy, incomplete, or sparse data.
    • Deployment of ML models to edge devices for real-time inference.
    • Accelerating ML training processes using GPU, TPU, or other HW accelerators.
    • Using reinforcement learning frameworks, e.g. Stable Baselines, RLlib, or similar.
  • General knowledge of robotics principles, including kinematics, dynamics, and control.
  • Publications or contributions to the machine learning community, particularly in areas related to robotics or reinforcement learning.

Apply