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Software Technical Lead, On-Device Inference Software, Robotics, DeepMind
GoogleSaint George, S.C., USASenior8+ years
Apply Minimum qualifications:
- Bachelor’s degree in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience.
- 8 years of experience leading and delivering full-stack system software projects.
- 3 years of experience with system-level software components such as operating systems, firmware, and compilers.
- Experience in software development with advanced embedded compute platform such as the Nvidia Jetson Thor, or Qualcomm IQ-X.
Preferred qualifications:
- Master’s degree or PhD in Computer Science, Electrical Engineering, Robotics, or a related field.
- Experience with on-device AI inference for robotics, automotive, or related use cases.
- Demonstrated engineering leadership experience, with a strong track record of driving large-scale, cross-organizational technical initiatives.
- Demonstrable expertise with JAX or PyTorch and related foundational technologies (XLA, CUDA).
- Familiarity with foundation models and the unique computational and security demands of running them in physical, interactive robotics environments.
- Strong communication skills with the ability to articulate complex safety requirements to various research, hardware, and software engineering teams.
About the job
At Google DeepMind our mission is to build the world's first general-purpose learning agent. Central to this mission is the complex task of measuring the intelligence of our prototypes. As a Software Engineer, you will be working with the cutting edge AI agents developed by our exceptional team of Machine Learning and Neuroscience research scientists. Your responsibilities will include everything from creating systems for agent testing using 2D and 3D games to developing test problems within physics simulators. You will create graphical visualization of results, build competitive agent leaderboards and test new algorithms on robots. To succeed in this role you will need to have a strong foundation in software engineering and enjoy working on a wide range of challenging problems within a mission-driven team.As a Technical Lead for On-Device Inference Software, you will take ownership of the software strategy and execution for on-robot compute solutions. You will be responsible for ensuring our advanced Gemini Robotics models run flawlessly and securely on cutting-edge embedded hardware. In this highly collaborative leadership position, you will bridge multiple teams, driving alignment between research and infrastructure to deliver a cohesive, high-performance system software stack from the OS level up to the model inference server.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 various 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: $207000 - $301000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Lead the software architecture and development for on-device inference, ensuring system readiness for external deployments.
- Coordinate and drive engineering efforts across multiple cross-functional teams to deliver critical system components.
- Ensure consistency, performance, and robust model security across the entire hardware and software integration layer.
- Optimize system software to run state-of-the-art Vision Language Action (VLA) models like Gemini Robotics efficiently and seamlessly on edge devices.
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