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.

Google Research drives fundamental breakthroughs, product and large-scale infrastructure and algorithmic advances. In this role, you will work at the frontier of machine learning, statistical inference, learning, and uncertainty quantification. You will guide multi-year research agendas that solve open-ended technical issues, inventing novel technologies and translating them into real-world autonomous systems. You will be actively involved in setting up experiments, demonstrating impact, and integrating and transferring new algorithms and models into products.

Google Research is building the next generation of intelligent systems for all Google products. To achieve this, we’re working on projects that utilize the latest computer science techniques developed by skilled software developers and research scientists. Google Research teams collaborate closely with other teams across Google, maintaining the flexibility and versatility required to adapt new projects and foci that meet the demands of the world's fast-paced business needs.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $262000 - $364000 (USD) + 25% bonus target + equity + benefits
Learn more about benefits at Google.

Responsibilities

• Scope, lead, and execute foundational research in learning, uncertainty estimation, and automated system diagnosis to enable adaptive perception and decision-making stacks.
• Develop novel methodologies for out-of-distribution (OOD) detection, active learning with demonstrable impact on behavior and semantics, multimodal retrieval, epistemic/aleatoric uncertainty quantification, and sensor-to-perception-to-action stack calibration.
• Partner with research leads, software engineers, and product teams to integrate breakthroughs into scalable, (and possibly real-time) autonomous infrastructure.
• Author high-impact papers in premier machine learning, computer vision, and robotics venues (e.g., CVPR, ECCV, NeurIPS, ICRA, IROS), contributing code, models, and datasets where applicable.

Minimum qualifications:

• Ph.D. in Computer Science, Robotics, Electrical Engineering, Statistics, or a related field (or equivalent practical experience).
• 6 years of experience leading research agendas, developing scalable algorithms, or deploying ML models in production or applied research environments.
• Experience with deep learning frameworks (e.g., JAX, TensorFlow) and with distributed training/evaluation pipelines.
• One or more scientific publication submission(s) for conferences, journals, or public repositories.(e.g., NeurIPS, ICML, CVPR, ECCV, ICCV, ICRA, CoRL).

Preferred qualifications:

• 6 years of experience leading complex research projects and setting technical direction in autonomous systems or mobile robotics.
• Experience designing, distilling, and optimizing neural architectures for low-latency, real-time edge hardware and onboard compute constraints.
• Proficiency in modern programming languages and working with large-scale distributed training infrastructure.
• Demonstrated track record in deep learning, epistemic/aleatoric uncertainty quantification, model calibration for decision-making, and out of distribution diagnosis and generalization. .
• Strong background in self-supervised learning, multimodal foundation models, latent and embedded spaces, or foundational representations.