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Entry Level Google Robotics Engineer Jobs in California

Research Assistant

San Mateo, CA · On-site

$150K - $200K/yr

You will work closely with ML researchers and robotics engineers to run robot experiments, design ... Google DeepMind, and other frontier labs-with a track record of shipping AI breakthroughs. Before ...

... optical engineering aspects of Neuralink surgeries, including working on our surgical robotics ... Basic computer skills and proficiency in Google Docs, Google Drive, and Gmail * Software ...

Its robots are engineered to perform a variety of tasks in the home and commercial markets. Figure ... prompt, Google Suite, in house development GUI's * Robotics experience * Experience using Jira ...

Its robots are engineered to perform a variety of tasks in the home and commercial markets. Figure ... prompt, Google Suite, in house development GUI's * Robotics experience * Experience using Jira ...

... Entry-level software engineers are expected to bring significant skills and training, and then to ... Experience with satellites, robots, or other autonomous systems * Previous experience on technical ...

... Entry-level software engineers are expected to bring significant skills and training, and then to ... Experience with satellites, robots, or other autonomous systems * Previous experience on technical ...

Programming experience in any language such as C/C++, VB, VBScript, Java, Perl, Python, Shell ... Our client list includes fortune 500 Companies like Google, Wells Fargo, EBay, Pay Pal, Cisco ...

Showing results 41-60

Entry Level Google Robotics Engineer information

Does Google hire entry level Google robotics engineers?

Google hires entry-level robotics engineers for roles involving robotics research, development, and engineering. These positions often require a relevant degree in engineering, computer science, or related fields, along with skills in programming, robotics systems, and problem-solving. Entry-level roles may also involve internships or apprenticeships for candidates with limited professional experience.

How to become an entry level Google robotics engineer?

To become an entry-level Google robotics engineer, candidates typically need a bachelor's degree in robotics, computer science, electrical engineering, or a related field. Gaining experience with programming languages like Python or C++, understanding robotics frameworks such as ROS, and developing skills in hardware integration are important. Internships or project work in robotics can also improve prospects for entry-level roles.

What are the most commonly searched types of Google Robotics Engineer jobs in California?

The most popular types of Google Robotics Engineer jobs in California are:

What are popular job titles related to Entry Level Google Robotics Engineer jobs in California?

For Entry Level Google Robotics Engineer jobs in California, the most frequently searched job titles are:

What job categories do people searching Entry Level Google Robotics Engineer jobs in California look for?

The top searched job categories for Entry Level Google Robotics Engineer jobs in California are:

Infographic showing various Entry Level Google Robotics Engineer job openings in California as of August 2026, with employment types broken down into 76% Full Time, 10% Part Time, and 14% Contract. Highlights an 100% In-person job distribution.

Research Assistant

Generalist AI, Inc

San Mateo, CA • On-site

$150K - $200K/yr

Full-time

Re-posted 21 days ago


Job description

About the Role:
We are looking for a Research Assistant to help design, run, and analyze experiments at the intersection of machine learning and robotics. This is an entry-level research role for individuals with less than 3 years of research experience, and is designed to be a potential career path towards eventually contributing as a Research Scientist.
At Generalist, we are building foundation models for robots. These models improve through a tight feedback loop: design experiments, collect data, train or fine-tune models, evaluate them in the real world, analyze results, and repeat. This role helps make that loop faster, more rigorous, and more reliable.
You will work closely with ML researchers and robotics engineers to run robot experiments, design evaluation tasks, brainstorm ideas, collect data, interpret results, and document repeatable workflows.
A major part of this role is helping ensure our evaluations are trustworthy. We care deeply about experimental design, controls, hands-on iteration, sample sizes, variance, repeatability, and statistical rigor.
You'll be responsible for:
  • Running structured experiments on robot platforms
  • Setting up physical tasks, materials, fixtures, and benchmarks for robot evaluations
  • Collecting high-quality robot data and tracking experimental conditions
  • Measuring real-world success rates across tasks, robots, and model variants
  • Designing evaluations with attention to controls, repeatability, statistical rigor, and sources of bias
  • Analyzing results to help distinguish real model improvements from noise
  • Synthesizing findings and communicating them clearly to ML researchers and engineers
  • Preparing robots, sensors, workspaces, and materials for rollouts and evaluations
  • Helping kick off training jobs, run evaluations, and organize results
  • Beta testing internal and third-party tools for teaching robots new skills
  • Troubleshooting physical setups, hardware issues, and procedural bottlenecks
  • Writing clear documentation and playbooks so others can reproduce workflows
  • Improving experimental reliability, data quality, and operational throughput over time
You might thrive in this role if you:
  • Have experience running experiments, lab studies, field studies, data collection workflows, or structured evaluations
  • Think carefully about experimental design, confounding factors, controls, sample sizes, variance, and what conclusions the data can actually support
  • Are diligent and detail-oriented, especially when tasks are repetitive but subtle differences matter
  • Enjoy hands-on work with physical systems, equipment, materials, or instruments
  • Are comfortable following protocols while also noticing when something is wrong or could be improved
  • Can coordinate many moving parts: robots, materials, tasks, data, model versions, metrics, and documentation
  • Communicate clearly and can summarize what happened, what changed, and what the evidence suggests
  • Are curious about machine learning and robotics, even if you are not yet an expert in either
  • Have some exposure to programming, data analysis, robotics, hardware, electronics, mechanical assembly, or experimental tooling
  • Prefer fast iteration, careful measurement, and empirical progress over abstract theory alone
About Generalist
At Generalist, we are on a mission to make general-purpose robots a reality. We believe the industries and homes of the future will depend on humans and machines working together in new ways. Robots can help us build more and get more done.
We build embodied foundation models, starting with a focus on dexterity. This requires advancing the frontiers of data, models, and hardware, to enable robots to intelligently interact with the physical world.
The company embraces both large-scale AI and robotics as core to its DNA. Our team of researchers, roboticists, and company builders come from OpenAI, Boston Dynamics, Google DeepMind, and other frontier labs-with a track record of shipping AI breakthroughs. Before Generalist, we pioneered large embodied multimodal models and vision-language-action models (PaLM-E, RT-2, Gemini Robotics), launched and scaled ChatGPT and GPT-4 to hundreds of millions of users, engineered the foundations of autonomous driving, built next-generation robots (Atlas, Spot, Stretch) and pushed the limits of what they can do (from parkour to manipulation, and testing robustness).
We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.