The role
From Google's own posting.
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.
To address complex human issues, our Robotics Team is seeking Research Scientists who can advance the frontier of embodied AI. In this role, you will focus on developing intelligent systems designed for meaningful physical world interaction, bridging the gap between theoretical algorithmic research and real-world deployment 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.
Architect and train models to expand robotic capabilities and prototype concepts.
Apply domain expertise in sim-to-real transfer, imitation learning, Vision-Language-Action (VLA) models, multi-sensory learning (tactile, audio), and model optimization (distillation, pruning, quantization).
Propose novel ideas, address manipulation hardware requirements, and communicate results to audiences.
Minimum qualifications:
PhD degree in a relevant quantitative field (e.g., Robotics, Computer Science, Machine Learning), or equivalent practical experience.
5 years of professional research experience in an industrial research and development (R&D) laboratory.
3 years of experience designing, training, and scaling reinforcement learning (RL), imitation learning (IL), or multimodal generative architectures (including Vision-Language-Action (VLA) or video foundation models).
2 years of experience optimizing foundation models for production, utilizing techniques like distillation, model pruning, and post-training quantization to meet on-device latency and memory footprint constraints.
Preferred qualifications:
3 years of experience building data pipelines, deploying production-ready systems, and experience managing multi-sensory datasets.
PhD degree in a relevant quantitative field (e.g., Robotics, Computer Science, Machine Learning), or equivalent practical experience.
5 years of professional research experience in an industrial research and development (R&D) laboratory.
3 years of experience designing, training, and scaling reinforcement learning (RL), imitation learning (IL), or multimodal generative architectures (including Vision-Language-Action (VLA) or video foundation models).
2 years of experience optimizing foundation models for production, utilizing techniques like distillation, model pruning, and post-training quantization to meet on-device latency and memory footprint constraints.