Robotics Engineer, Controls and Autonomy, DeepMind.

Minimum qualifications:

• Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
• 2 years of experience with software development in one or more programming languages.
• 2 years of experience in developing and debugging robotic software systems.
• 2 years of experience with physics-based simulation environments, specifically MuJoCo, for model development and testing.
• 1 year of experience developing behaviors for mobile robotic systems.

Preferred qualifications:

• 4 years of experience in developing and debugging robotic software systems.
• Proven expertise in Reinforcement Learning (RL) for robotic locomotion and physical control.
• In-depth understanding of safe system design for physical-software interaction during autonomous operation.
• Strong background in robotic kinematics, dynamics, and classical control theory.

About the job:

Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.

We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities:

• Design and implement control systems for robotic platforms, focusing on executing long-horizon, fully autonomous behaviors to put cycles on new hardware platforms.
• Optimize control loops and real-time software to ensure low-latency response and stability during complex physical interactions.
• Work within an established internal simulation based RL pipeline to accelerate design insights.
• Collaborate with the hardware and AI teams to define requirements for future robotic systems.
• Establish software development best practices, including high test coverage and modular code architecture for behavioral stacks.