Research Scientist, Efficient AI

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.

Our team develops resource-efficient architectures, model training/inference recipes, and model compression techniques to make machine learning models faster, smaller, and better. Our mission is to push the boundaries of model efficiency, enabling Google's flagship models to run efficiently across Google’s massive server-side deployments.

Google Research addresses challenges that define the technology of today and tomorrow. From conducting fundamental research to influencing product development, our research teams have the opportunity to impact technology used by billions of people every day.

Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field -- we publish regularly in academic journals, release projects as open source, and apply research to Google products.

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.

Responsibilities

• Propose and drive independent research directions, and manage a strong research agenda, communicating with and participating with the broader research community.
• Refine your ideas in close collaboration with the team and cross-organization partners.
• Translate ideas to experiments swiftly, and be able to reason about model efficiency (latency, parameters, memory, I/O, compute efficiency, etc.).
• Communicate your results to the team regularly and seek feedback.

Minimum qualifications:

• PhD degree in Computer Science, a related field, or equivalent practical experience.
• Experience in ML/AI, backed by a publication record in conferences such as NeurIPS, ICML, ICLR, CVPR, etc.
• Experience in Python and modern ML frameworks, such as JAX and PyTorch.
• Experience writing model training and inference pipelines.

Preferred qualifications:

• Experience in model optimization, including knowledge distillation, model architectures, quantization, etc.
• Experience profiling model latency, I/O, and other bottlenecks while proposing relevant optimizations.
• Experience with low-level optimization.
• Understanding of TPU/GPU architectures, memory bandwidth constraints, and compiler-level optimizations (XLA).
• Ability to grow in a fast-paced environment that demands scientific excellence while ensuring ideas can be experimented with and deployed in production.