Robotics Platform Software Engineer, DeepMind
Summary
Builds the hardware-facing software stack for Google DeepMind's embodied AI robots: real-time OS tuning, memory management, and low-latency communication across the robot stack, plus CI/CD and hardware-in-the-loop testbenches. Core tech includes RTOS, CAN/SPI/I2C/UART protocols, and simulation tools like MuJoCo, Isaac Sim, and Gazebo.
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.
US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
- Design and develop robust software for real-time robotic workloads, including RTOS tuning and memory management.
- Build and maintain CI/CD infrastructure and hardware-in-the-loop (HIL) testbenches to support rapid development cycles.
- Establish systems, tools, and processes for high-velocity robot development, including logging and data visualization.
- Ensure low-latency communication and system stability across the entire robot stack.
- Establish a culture of following software development best practices, including modularity, well documented code, and high test coverage.
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 5 years of experience with software development in one or more programming languages.
- 5 years of experience developing and debugging robotic software systems and supporting toolchains.
- 3 years of experience testing, maintaining, or launching core components of a robotics software stack, and 1 year of experience with software design and architecture of a robotics software stack.
- 3 years of experience with processor and memory optimization, real-time operating systems (RTOS), and communication protocols (e.g., CAN, SPI, I2C, UART).
Preferred qualifications:
- Experience with GPU/TPU optimization for model inference and sensor fusion.
- Experience with system simulation tools such as Mujoco, NVIDIA Isaac Sim, or Gazebo.
- Knowledge of robotic system design, including kinematics, dynamics, control algorithms, and perception systems.
- Ability to deliver high-quality and durable software with appropriate test coverage.
- Insights into how safety impacts system design across physical and software layers.
