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Machine Learning Robotics Jobs in California (NOW HIRING)

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 · 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 ...

DiDi Labs

Machine Learning Engineer, VLA

San Jose, CA · On-site

$129K - $247K/yr

Strong background in machine learning, deep learning, or robotics * Experience with PyTorch / JAX / TensorFlow * Solid understanding of modern neural architectures (transformers, diffusion, auto ...

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 ...

DiDi Labs

Machine Learning Engineer, VLA

San Jose, CA

$129K - $247K/yr

Strong background in machine learning, deep learning, or robotics * Experience with PyTorch / JAX / TensorFlow * Solid understanding of modern neural architectures (transformers, diffusion, auto ...

Meta

AI Research Scientist, Robotics

Menlo Park, CA

$184K/yr

AI Research Scientist, Robotics Responsibilities: * Perform fundamental and applied research to ... Develop algorithms based on state-of-the-art machine learning and neural network methodologies

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Machine Learning Robotics information

See California salary details

$25.2K

$42K

$86.8K

How much do machine learning robotics jobs pay per year?

As of Jun 5, 2026, the average yearly pay for machine learning robotics in California is $42,026.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,100.00 and $45,400.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Machine Learning Robotics position, and why are they important?

To excel in Machine Learning Robotics, a strong background in computer science, robotics, and machine learning algorithms, often supported by a relevant degree (such as in engineering or computer science), is essential. Familiarity with programming languages like Python or C++, frameworks such as TensorFlow or ROS (Robot Operating System), and experience with simulation tools are highly valuable, and certifications in AI or robotics can further enhance employability. Strong problem-solving skills, effective communication, and the ability to work collaboratively in cross-disciplinary teams help professionals stand out. These capabilities are crucial for designing, developing, and refining intelligent robotic systems that perform reliably in real-world environments.

What are some common challenges faced by professionals working in Machine Learning Robotics?

Professionals in Machine Learning Robotics often encounter challenges like integrating machine learning models with robotic hardware, ensuring reliable performance in unpredictable real-world settings, and managing computational limitations on embedded systems. Addressing these challenges usually requires creative problem-solving and close collaboration with hardware engineers, software developers, and data scientists. You may also need to continuously refine models and testing processes based on real-time feedback and evolving project goals. This dynamic environment makes the work both demanding and highly rewarding for those eager to push the boundaries of automation and intelligent systems.

What is a Machine Learning Robotics job?

A Machine Learning Robotics job involves developing algorithms that enable robots to learn from data and improve their performance over time. Professionals in this field work on applications such as autonomous navigation, robotic perception, and human-robot interaction. They use techniques like deep learning, reinforcement learning, and computer vision to enhance a robot's ability to understand and interact with its environment. This role typically requires expertise in machine learning, robotics, and software development, along with strong problem-solving skills.

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 are popular job titles related to Machine Learning Robotics jobs in California? For Machine Learning Robotics jobs in California, the most frequently searched job titles are:
What job categories do people searching Machine Learning Robotics jobs in California look for? The top searched job categories for Machine Learning Robotics jobs in California are:
What cities in California are hiring for Machine Learning Robotics jobs? Cities in California with the most Machine Learning Robotics job openings:
Machine Learning Robotics jobs near you
Infographic showing various Machine Learning Robotics job openings in California as of May 2026, with employment types broken down into 80% Full Time, 8% Part Time, 6% Temporary, and 6% Contract. Highlights an 94% In-person, and 6% Remote job distribution, with an average salary of $42,026 per year, or $20.2 per hour.

Machine Learning Engineer - Robot Perception

Maven Robotics

San Francisco, CA • On-site

Full-time

Posted 5 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 Perception to design, implement, test, and deploy robot perception algorithms that power our robots' ability to understand and interact with the world.
In this role you will:
  • Develop, train, and deploy ML-based perception algorithms for object detection, pose estimation, tracking, and scene understanding.
  • Integrate sensor fusion techniques using cameras, depth sensors, IMUs, and tactile feedback.
  • Optimize real-time perception pipelines for low-latency and robust performance in dynamic environments.
  • Work closely with hardware engineers to design sensor configurations and optimize perception models for onboard deployment.
  • Contribute to our broader AI and autonomy stack, ensuring seamless integration with reasoning, manipulation, planning and control.
  • 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 background in computer vision, deep learning, and sensor fusion.
  • Proficiency in Python and C++, with experience in frameworks like PyTorch, TensorFlow, OpenCV, and ROS.
  • Hands-on experience with real-world robotics perception systems (e.g., SLAM, 3D reconstruction, multimodal perception).
  • Experience working with hardware, including setting up and calibrating cameras, LiDAR, and other sensors.
  • 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:
    • Developing models that can handle noisy, incomplete, or sparse data.
    • Deployment of ML models to edge devices for real-time inference (e.g., NVIDIA Jetson).
    • Accelerating ML training processes using GPU, TPU, or other HW accelerators.
    • General knowledge of robotics principles, including kinematics, dynamics, and control.

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