At a Glance
- Tasks: Design AI systems for code correctness and safety using cutting-edge proof assistants.
- Company: Join Google DeepMind, a pioneering AI lab focused on transformative technology.
- Benefits: Competitive salary, bonuses, equity, and comprehensive benefits package.
- Other info: Collaborative environment with diverse career pathways and learning opportunities.
- Why this job: Make a real impact in AI development and contribute to global issues.
- Qualifications: PhD in relevant field and experience with programming languages and formal methods.
The predicted salary is between 165000 - 225000 £ per year.
By applying to this position you will have an opportunity to share your preferred working location from the following: London, UK; Mountain View, CA, USA.
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).
About the job:
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.
Research Scientist, Verified Code Generation, DeepMind in London employer: Google
At Google, we pride ourselves on fostering a dynamic and inclusive work culture that empowers our employees to thrive. As a Global Threat Intelligence Analyst, you will benefit from unparalleled growth opportunities, access to cutting-edge resources, and the chance to collaborate with industry leaders in a vibrant location that champions innovation and security excellence.
StudySmarter Expert Advice🤫
We think this is how you could land Research Scientist, Verified Code Generation, DeepMind in London
✨Join Local Tech Meetups
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✨Contribute to Open Source Projects
Show off your coding chops by jumping into open-source projects. Not only does this give you practical experience, but it also gets you noticed in the dev community. You'll create a killer portfolio that speaks volumes about your skills to Google.
✨Tap into Online Developer Communities
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We think you need these skills to ace Research Scientist, Verified Code Generation, DeepMind in London
Some tips for your application 🫡
Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at Google.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Google and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!
How to prepare for a job interview at Google
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
✨Know Your Tools and Frameworks
Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If Google uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
✨Showcase Your Projects
Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.
