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Entry Level Machine Learning Robotics Jobs in California

Skild AI

Research Engineer

San Mateo, CA · On-site

In this role, you'll work with researchers and engineers to implement state-of-the-art machine learning and robot systems. The ideal candidate has a strong technical background, experience in turning ...

Human Archive

Machine Learning Engineer

San Francisco, CA · On-site

$100K - $150K/yr

Today, our data is used for robotics and world modeling, but the broader opportunity is advancing ... The Opportunity As a Machine Learning Engineer, you'll work on multimodal perception, VLA training ...

Skild AI

Research Scientist

San Mateo, CA · On-site

We are looking for people with proven expertise in machine learning and/or robotics, who are passionate about pushing the boundaries of what is possible. You will collaborate with a team of talented ...

Bosch Group

Research Scientist- Robotics AI

Sunnyvale, CA · On-site

  • Medical

  • Life

  • Retirement

  • PTO

As a Research Scientist- Robotics AI, you contribute to research projects at the forefront of the ... Conduct research and engineering in core AI and machine learning fields to enable Embodied AI ...

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Entry Level Machine Learning Robotics information

What is the difference between Entry Level Machine Learning Robotics vs Entry Level Data Scientist?

AspectEntry Level Machine Learning RoboticsEntry Level Data Scientist
Required CredentialsBachelor's in CS, Robotics, or related; knowledge of ML, programmingBachelor's in CS, Statistics, or related; strong analytical skills
Work EnvironmentRobotics labs, manufacturing, research facilitiesCorporate offices, research firms, tech companies
Industry UsageManufacturing, automation, robotics developmentFinance, healthcare, tech, marketing
Common Search/ComparisonYesYes

Entry Level Machine Learning Robotics focuses on developing and programming robotic systems using machine learning techniques, often in manufacturing or research settings. Entry Level Data Scientist emphasizes analyzing data to inform business decisions across various industries. While both roles require programming and analytical skills, their work environments and applications differ significantly.

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:

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Entry Level Machine Learning Robotics jobs near you
Infographic showing various Entry Level Machine Learning Robotics job openings in California as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 18% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Machine Learning Engineer, Reinforcement Learning

Skild AI

San Mateo, CA • On-site

Full-time

Re-posted 10 days ago

Job description

Position Overview

We are looking for a Machine Learning Engineer to be responsible for designing and implementing cutting-edge reinforcement learning algorithms, conducting experiments, and optimizing these models to perform efficiently in real-world robotic environments. This will require close collaboration with our robotics, research, and engineering team. Your work will directly impact the development of intelligent, adaptable robots capable of learning and performing complex tasks autonomously.

Responsibilities
  • Develop and implement state-of-the-art reinforcement learning algorithms for robotic applications.
  • Design and conduct experiments to train RL models and conduct real-world tests.
  • Collaborate closely with researchers to explore novel methods of scaling up reinforcement learning model training.
  • Communicate effectively with inference, application, and deployment engineers to integrate RL models into robotic systems and iterate on methods to enable robust deployment.
  • Analyze and interpret experimental results, iterating on model design to achieve desired performance.
  • Stay up-to-date with the latest research and advancements in reinforcement learning.
Preferred Qualifications
  • BS, MS or higher degree in Computer Science, Robotics, Engineering or a related field, or equivalent practical experience.
  • Proficiency in Python, C++, or similar and at least one deep learning library such as PyTorch, TensorFlow, JAX, etc.
  • Deep understanding and practical experience with various reinforcement learning algorithms and techniques (model-free, model-based, multi-task, hierarchical, multi-agent, etc.).
  • Strong background in algorithms, data structures, and software engineering principles.
  • Experience with physics simulation engines and tools for training RL.
  • Deep understanding of state-of-the-art machine learning techniques and models.
  • Extensive industry experience with reinforcement learning and robotic systems.

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