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.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 global issues 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 various 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
- Design and implement AI systems that produce formal proofs of code correctness, safety, and security using the Lean proof assistant.
- Formalize programming language semantics in Lean to enable verified static analysis of real-world codebases.
- Prototype and evaluate novel techniques combining Large Language Model (LLMs) with formal verification for automated code analysis and generation.
- Build tools, libraries, and infrastructure to scale formal verification to large codebases.
- Collaborate with researchers and engineers across AI, security, and compiler infrastructure teams.
Qualifications
Minimum qualifications:
- PhD degree in computer science, programming languages, formal methods, software engineering, or a related technical field, or equivalent practical experience.
- 4 years of experience in one or more of the following: programming language semantics, static analysis, abstract interpretation, software verification, or interactive theorem-proving.
- 1 year of experience with a proof assistant (Lean, Coq, Isabelle, or similar).
Preferred qualifications:
- 2 years of experience with compiler infrastructure (Low Level Virtual Machine (LLVM) or GNU Compiler Collection (GCC)) or programming language formalization.
- 1 year of experience with large language models or machine learning for code or reasoning tasks.
- 1 year of experience in memory safety analysis, vulnerability research, or systems security.
- 1 year of experience with Lean 4.
- Experience building and scaling software verification tools for production codebases.
- Publication record at top formal methods and software security venues (e.g., POPL, PLDI, CCS, or S&P).