At a Glance
- Tasks: Join us to develop AI systems for code analysis and generation in a collaborative environment.
- Company: Google DeepMind is at the forefront of AI, focusing on public benefit and scientific discovery.
- Benefits: Enjoy a supportive culture, opportunities for learning, and the chance to work with cutting-edge technology.
- Why this job: Be part of an innovative team pushing boundaries in AI and software engineering.
- Qualifications: Masters in computer science or related field; experience in ML, Python, and code analysis tools required.
- Other info: Diversity is valued here; we encourage applicants from all backgrounds.
The predicted salary is between 28800 - 48000 ÂŁ per year.
Artificial Intelligence could be one of humanity’s most useful inventions. At Google DeepMind, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence. We use our technologies for widespread public benefit and scientific discovery, and collaborate with others on critical challenges, ensuring safety and ethics are the highest priority.
We’re a dedicated scientific community, committed to “solving intelligence” and ensuring our technology is used for widespread public benefit. We’ve built a supportive and inclusive environment where collaboration is encouraged and learning is shared freely. We don’t set limits based on what others think is possible or impossible. We drive ourselves and inspire each other to push boundaries and achieve ambitious goals.
To succeed in this role you will need to be passionate about advancing AI systems that can rigorously analyze correctness, safety, and security properties of code and generate code that satisfies these properties by construction. You will join an energetic team of computer scientists, ML researchers and engineers. You will leverage our unique mix of expertise, data and computational resources to experiment and iterate both rapidly and at scale. As an embedded Research Engineer you will collaborate with research scientists and software engineers to develop and run experiments, exploring ways to develop AI systems for code analysis and generation. The team is exploring many different foundational and applied challenges in software engineering and security, so you may take part in exploratory work validating early ideas or work in a maturing area to deepen and exploit a promising line of research. You will also contribute to the knowledge and experience of the team with your own knowledge.
Key responsibilities:
- Plan and perform rapid prototyping of ML-based techniques for automatically analyzing and generating code.
- Undertake exploratory analysis to inform experimentation and research directions.
- Make improvements to model architectures and training procedures of machine learning models used for code analysis.
- Implement tools, libraries and frameworks to speed up and enable new research.
- Report and present software developments, experimental results and data analysis clearly and efficiently.
- Collaborate with internal and external domain experts.
The role will suit candidates who enjoy working in a heavily experimental setting with large and noisy datasets and who wish to immerse themselves in innovative science, ML and AI research.
About You
In order to set you up for success as a Research Engineer at Google DeepMind, we look for the following skills and experience:
- Masters degree in computer science, electrical engineering, mathematics, or equivalent experience.
- Understanding of foundational principles of code analysis (e.g., invariant generation, abstract interpretation).
- Experience building tools for code analysis and generation.
- Experience with Python and preferably Rust and C++.
- Applied experience with machine learning, preferably modern deep learning techniques (e.g. Transformers, Diffusion Models, LLMs) and reinforcement learning.
- Experience exploring, analysing and visualising data.
- Experience using Jax, PyTorch, TensorFlow, NumPy, Pandas or similar ML libraries.
In addition, the following would be an advantage:
- Experience with formal mathematical reasoning, and in particular, the Lean theorem-prover.
- Prior experience building AI-based tools for code analysis and generation.
- Experience working with large and noisy datasets.
- Experience collaborating across fields.
- Broad-based knowledge of current trends in computer science.
When assessing technical background we will take a holistic view of the mix of experience with ML, engineering experience, and foundational computer science. We do not expect you to be an expert in all fields simultaneously. However, since the role serves as a bridge between all three, some experience in each is necessary.
At Google DeepMind, we value diversity of experience, knowledge, backgrounds and perspectives and harness these qualities to create extraordinary impact. We are committed to equal employment opportunity regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy, or related condition (including breastfeeding) or any other basis as protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know.
Applications close on Monday 14th July @ 5pm UK Time.
Research Engineer, Code Analysis employer: The Rundown AI, Inc.
Contact Detail:
The Rundown AI, Inc. Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Research Engineer, Code Analysis
✨Tip Number 1
Familiarise yourself with the latest advancements in AI and machine learning, particularly in code analysis. This will not only help you understand the role better but also allow you to engage in meaningful conversations during interviews.
✨Tip Number 2
Network with professionals in the field of AI and code analysis. Attend relevant conferences, webinars, or meetups to connect with experts and learn about their experiences, which can provide valuable insights for your application.
✨Tip Number 3
Showcase your hands-on experience with tools and libraries mentioned in the job description, such as Jax, PyTorch, or TensorFlow. Consider contributing to open-source projects or creating your own projects to demonstrate your skills.
✨Tip Number 4
Prepare to discuss your approach to problem-solving and experimentation in a collaborative environment. Think of examples from your past work where you successfully tackled challenges and contributed to team goals, as this aligns with the role's responsibilities.
We think you need these skills to ace Research Engineer, Code Analysis
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in machine learning, code analysis, and programming languages like Python, Rust, and C++. Emphasise any projects or roles that demonstrate your ability to work with large datasets and your understanding of foundational principles of code analysis.
Craft a Compelling Cover Letter: In your cover letter, express your passion for AI and how it aligns with the mission of Google DeepMind. Discuss specific experiences that showcase your skills in rapid prototyping, collaboration with domain experts, and your approach to problem-solving in experimental settings.
Showcase Relevant Projects: Include a section in your application that details any relevant projects you've worked on, particularly those involving machine learning techniques or tools for code analysis and generation. Highlight your role, the technologies used, and the outcomes of these projects.
Prepare for Technical Questions: Be ready to discuss your technical background in detail. Review key concepts in machine learning, code analysis, and any relevant programming languages. Prepare to explain your thought process in previous projects and how you approached challenges in a collaborative environment.
How to prepare for a job interview at The Rundown AI, Inc.
✨Show Your Passion for AI
Make sure to express your enthusiasm for artificial intelligence and its applications. Discuss any personal projects or experiences that demonstrate your commitment to advancing AI systems, especially in code analysis and generation.
✨Demonstrate Technical Knowledge
Be prepared to discuss foundational principles of code analysis and your experience with relevant tools and languages like Python, Rust, and C++. Highlight specific projects where you've applied machine learning techniques, particularly modern deep learning methods.
✨Prepare for Collaborative Scenarios
Since the role involves collaboration with various experts, think of examples where you've successfully worked in a team. Be ready to discuss how you approach problem-solving in a collaborative environment and how you can contribute to a supportive team culture.
✨Discuss Your Experimental Approach
Given the experimental nature of the role, be ready to talk about your experience with rapid prototyping and exploratory analysis. Share insights on how you've iterated on ideas and adapted to challenges when working with large datasets.