At Google, research-focused Software Engineers are embedded throughout the company, allowing them to setup large-scale tests and deploy promising ideas quickly and broadly. Ideas may come from internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.
From creating experiments and prototyping implementations to designing new architectures, engineers work on real-world problems including artificial intelligence, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more. But you stay connected to your research roots as an active contributor to the wider research community by partnering with universities and publishing papers.
At Google DeepMind Robotics, we are pioneering AI for the physical world, enabling robots to perceive, plan, think, use tools, and act to solve complex tasks. We develop advanced Vision Language Action (VLA) models that combine Gemini’s world understanding with physical actions. This includes Gemini Robotics (our most advanced physical-world model) and Gemini Robotics On-Device (our fastest model, running without a data network). We also build advanced reasoning and agentic systems like Gemini Robotics-ER, featuring sophisticated spatial understanding and agentic reasoning for long, multi-step tasks.
We push boundaries in general-purpose robotics, including action generalization, human-robot interaction, dexterity, whole-body control, and continual learning. We partner with key robotics companies to bring this intelligence to the physical world across a broad range of applications at scale.
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: $207000 - $301000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Lead the development, integration, and deployment of robot navigation solutions (both learned/VLA-based and classical) for internal research and external partner use cases.
- Own the life-cycle of navigation solutions from conceptualization and simulation testing to real-world deployment on various robotic embodiments.
- Drive and guide data collection initiatives to support navigation model training, including defining data requirements, establishing collection protocols, and ensuring data quality.
- Oversee and participate in training, fine-tuning, and optimizing frontier machine learning models (e.g., Gemini-based VLAs) specifically for navigation and spatial reasoning tasks.
- Collaborate closely with external partners to understand their target environments, support the deployment of Google DeepMind (GDM) navigation solutions, and iteratively improve models based on real-world feedback.
Qualifications
Minimum qualifications:
- Bachelor’s degree in Computer Science, Robotics, or equivalent practical experience.
- 4 years of experience in a technical role (e.g., Software Engineering, Research Engineering, AI/ML Engineering, or Solutions Architecture).
- 2 years of experience deploying systems for medium and long range robot navigation, either learned or reliant on classical methods such as Service Level Availability siMulation (SLAM).
- Experience with machine learning tools and algorithms, specifically Large Language Models (LLMs)/Vision Language Models (VLMs) and deep learning.
Preferred qualifications:
- Experience with ROS/ROS2, simulation environments (Isaac Sim, MuJoCo), or on-device deployment (Jetson, TPU).
- Excellent Python programming skills.
- Track record of owning technical problems end-to-end and navigating the ambiguity of a fast-paced research environment.
- Passion for the future of embodied AI, and a desire to enable the success of the global developer community.