Research Scientist, Robotics
Google· Internet & Cloud Services· Sep 1, 2026 · 2mo ago
Kirkland, WA, USA
In office
Full-time
Mid
$117K–$176K est.
2+ years exp
Job description
As an organization, Google maintains a portfolio of research projects driven by fundamental research, new product innovation, product contribution and infrastructure goals, while providing individuals and teams the freedom to emphasize specific types of work. As a Research Scientist, you'll setup large-scale tests and deploy promising ideas quickly and broadly, managing deadlines and deliverables while applying the latest theories to develop new and improved products, processes, or technologies. From creating experiments and prototyping implementations to designing new architectures, our research scientists work on real-world problems that span the breadth of computer science, such as machine (and deep) learning, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more.
As a Research Scientist, you'll also actively contribute to the wider research community by sharing and publishing your findings, with ideas inspired by internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.The Google Cloud AI Research team addresses AI challenges motivated by Google Cloud’s mission of bringing AI to tech, healthcare, finance, retail and many other industries. We work on a range of unique problems focused on research topics that maximize scientific and real-world impact, aiming to push the state-of-the-art in AI and share findings with the broader research community. We also collaborate with product teams to bring innovations to real-world impact that benefits our customers. Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google .
• Develop novel techniques for robotic grasping, dexterous manipulation, planning, navigation, and 3D spatial understanding in dynamic physical environments.
• Design scalable teleoperation systems, portable data capture interfaces, and agent-assisted workflows to drive robot learning.
• Create advanced sim-to-real frameworks to enable scalable, generalized policy learning.
• Architect, test, and deploy robust hardware-software robotic systems to automate complex, high-precision physical workflows under real-world constraints.
• Partner with researchers, hardware specialists, and engineering teams to push the frontiers of physical AI across Google.
• PhD in Computer Science or robotics related fields, or equivalent practical experience.
• 2 years of experience in research across robotics domains, such as navigation, locomotion, manipulation, data collection for robot learning, sim-to-real transfer, vision-language-action model, or world action model development.
• One of more scientific publication submission(s) for conferences, journals, or public repositories (NeurIPS, ICML, ICLR, CoRL, RSS, ICRA, IROS, CVPR, ECCV, ICCV, etc.
• Experience coding in Python or C++.
Minimum qualifications:
• PhD in Computer Science or robotics related fields, or equivalent practical experience.
• 2 years of experience in research across robotics domains, such as navigation, locomotion, manipulation, data collection for robot learning, sim-to-real transfer, vision-language-action model, or world action model development.
• One of more scientific publication submission(s) for conferences, journals, or public repositories (NeurIPS, ICML, ICLR, CoRL, RSS, ICRA, IROS, CVPR, ECCV, ICCV, etc.
• Experience coding in Python or C++.
Preferred qualifications:
• 2 years of coding experience in Python or C++, as well as software frameworks such as PyTorch, JAX, ROS2, OpenCV.
• 1 year of experience owning and initiating research agendas.
• Experience building and scaling real-world robotic systems end-to-end, spanning custom data collection pipelines, model training, and physical deployment.
• Demonstrated ability to bridge theoretical algorithms with physical robotic hardware or high-fidelity simulation environments.
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