Role overview
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.
Labs is a group focused on incubating early-stage efforts in support of Google’s mission to organize the world’s information and make it universally accessible and useful. Our team exists to help discover and create new ways to advance our core products through exploration and the application of new technologies. We work to build new solutions that have the potential to transform how users interact with Google. Our goal is to drive innovation by developing new Google products and capabilities that deliver significant impact over longer timeframes.
The US base salary range for this full-time position is $147,000-$211,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.
What you'll work on
- Design and execute high-throughput strategies to capture high-quality multi-view video and image data from thousands of unique participants and environments.
- Design and optimize specialized acquisition hardware and optical configurations to extract high-precision ground truth visual data for complex foreground subjects and environmental backgrounds.
- Research and implement methods to fine-tune generative video foundation models on proprietary datasets to produce high-fidelity synthetic video training data, dramatically increasing model exposure to scenes.
- Develop automated pipelines that generate high-resolution depth, segmentation, and motion labels from production-grade models to supervise and train next-generation research architectures.
- Create rigorous, large-scale image and video evaluation datasets specifically designed to measure and solve "long-tail" quality issues, such as complex material properties and temporal stability.
Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.
What we're looking for
- Experience with generative video research, including fine-tuning or distillation of foundation vision models for novel view synthesis.
- Familiarity with distributed training frameworks and large-scale machine learning data infrastructure.
- Expertise in computational photography or specialized sensor calibration, such as active illumination or multi-modal sensor fusion.
- Proven track record of managing end-to-end visual data pipelines, from initial capture strategy to automated curation and model integration.