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Machine Learning Robotics Jobs in California (NOW HIRING)

Jobtailor

ML Engineer - Robotics

Mountain View, CA · On-site

$150 - $200/hr

Experience with ROS or ROS2 and integrating ML models into robotics software stacks for live deployments. * 3-10 years of experience in machine learning, robotics, or computer vision. * Strong grasp ...

FieldAI

2.53 3D Machine Learning Engineer

Irvine, CA · On-site

FieldAI is a company based in Irvine, California, specializing in embodied AI and robotics. They are seeking a 3D Machine Learning Engineer to design, implement, and maintain advanced 3D machine ...

FieldAI

3D Machine Learning Engineer

Irvine, CA · On-site

$150K - $200K/yr

FieldAI's Irvine team is where embodied AI meets real robots, real sensors, and real field ... What You'll Do * Design and implement scalable machine learning pipelines for large-scale 3D ...

FieldAI

3D Machine Learning Engineer

Irvine, CA

$150K - $200K/yr

FieldAI's Irvine team is where embodied AI meets real robots, real sensors, and real field ... What You'll Do * Design and implement scalable machine learning pipelines for large-scale 3D ...

FieldAI

3D Machine Learning Engineer

Irvine, CA · On-site

$150K - $200K/yr

FieldAI's Irvine team is where embodied AI meets real robots, real sensors, and real field ... What You'll Do * Design and implement scalable machine learning pipelines for large-scale 3D ...

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All JobsMachine Learning Robotics JobsMachine Learning Robotics Jobs in California

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 Sep 8, 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 is a machine learning robotics?

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 key skills and qualifications needed to thrive in machine learning robotics?

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 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 September 2026, with employment types broken down into 1% Internship, 1% As Needed, 59% Full Time, 38% Part Time, and 1% Contract. Highlights an 82% Physical, 3% Hybrid, and 15% Remote job distribution, with an average salary of $42,026 per year, or $20.2 per hour.

Perception Machine Learning Engineer - Continuous Learning

Waymo

San Diego, CA • On-site

Full-time

Posted 24 days ago

Job description

As a Perception Machine Learning Engineer, you will build the intelligent systems that "see" the world, directly shaping the future of autonomous travel.

Within the Perception team, we are tackling some of the most complex, open-ended challenges in autonomous driving. Our models must constantly adapt and improve as our fleet encounters the vast, unpredictable realities of public roads. We are looking for a Machine Learning Engineer to help design and build the automated, closed-loop systems that drive this continuous improvement.

In this role, you will be the bridge between model architecture and large-scale data infrastructure. You will leverage active learning and sophisticated data curation strategies to ensure our perception models are always learning from the most informative examples. Crucially, this means managing the entire lifecycle of our data: intelligently selecting novel scenarios from the fleet while continuously pruning our existing corpus to maximize training efficiency.

In this hybrid role you will report to a Technical Lead Manager.

You will:

  • Architect Infrastructure: Design and scale the data pipelines needed to mine, ingest, and manage massive volumes of sensor data from our fleet.
  • Drive Model Improvement: Deploy active learning algorithms to continuously identify and select the most impactful data for training, ensuring our large models continuously adapt to new environments with incremental updates.
  • Ensure Model Quality: Develop methods and recipes for evaluating real-world performance of our models, and detecting regressions in model updates.  Develop and maintain ground-truth free performance metrics.
  • Optimize Data Efficiency: Conduct large-scale experiments focused on data balancing, subset selection, and label quality optimization. Lead automated curation strategies-including smart pruning and downsampling-to minimize dataset bloat and maximize compute efficiency.
  • Solve Long-Tail Challenges: Develop robust mining, training and evaluation pipelines for rare, safety-critical real-world scenarios.
  • Innovate with Model Signals: Utilize uncertainty estimation, confidence scores, and embedding space analysis to uncover model blind spots and guide automated data acquisition.
  • Collaborate Cross-Functionally: Work closely with researchers and operations teams to iterate on the end-to-end model development lifecycle.

You Have:

  • A bachelor's degree in Machine Learning, Robotics, or Computer Science.
    3+ years of professional experience in Machine Learning and/or Computer Vision.
  • Proven, hands-on experience applying active learning in production environments.
  • Strong expertise in building large-scale ML data pipelines (mining, extraction, auto-labeling, ingestion).
  • Deep understanding of data curation-balancing, core set selection, and sampling-to optimize model performance.
  • Proficiency in Python and deep learning frameworks (PyTorch or JAX).
  • Strong software engineering skills for writing robust, production-ready code.

We Prefer:

  • An advanced degree (MS or PhD) in Machine Learning, Robotics, or Computer Science.
  • A record of publications at top-tier conferences (e.g., CVPR, ICCV, ECCV, ICML, ICLR, NeurIPS, IROS, RSS, AAAI, IJCV, PAMI).
  • Experience with C++
  • Experience building data-centric infrastructure from the ground up to accelerate model iteration cycles.

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