Research Engineer, Adaptivity and Reliability Team

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.

Snapshot

At Google DeepMind, we've built a unique culture and work environment where long-term ambitious research can flourish. Our special interdisciplinary team combines the best techniques from deep learning, reinforcement learning and systems neuroscience to build general-purpose learning algorithms. We have already made a number of high profile breakthroughs towards building artificial general intelligence, and we have all the ingredients in place to make further significant progress over the coming year!

About us

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.

We constantly iterate on our workplace experience with the goal of ensuring it encourages a balanced life. From excellent office facilities through to extensive manager support, we strive to support our people and their needs as effectively as possible.

Our list of benefits is extensive, and we’re happy to discuss this further throughout the interview process.

The team

The objective of the Adaptivity and Reliability Team (ART) is to enable and facilitate the use of AI systems in practice. We focus on targeted research efforts to improve reliability of models and their ability to adapt to different needs and contexts. 

Our research is grounded in applications with a potential for broad impact. We seek opportunities to develop and demonstrate capabilities of AI in collaboration with other teams in Google DeepMind. We build expertise and gain insight on applications by working on products that can benefit from our research, which in turn inspires further research.

The role

Research Engineers work on a diverse and stimulating range of projects including: developing algorithms and prototype applications, providing software design and programming support to research projects, along with architecting and implementing software libraries.

Key responsibilities:

• Engage in team collaborations to meet ambitious research goals.
• Design, implement, and evaluate models, agents and software prototypes for real world AI.
• Architect and implement software libraries.
• Bridge the gap between fundamental research and products by addressing research questions arising in real world problems and integrating novel research into applications.
• Report and present research findings and developments including status and results clearly and efficiently both internally and externally, verbally and in writing.
• Work in collaboration with our Ethics and Governance teams to ensure our advances in intelligence are developed ethically and provide broad benefits to humanity.

About you:

To set you up for success as a Research Engineer at Google DeepMind, we look for the following skills and experience:

• MSc/MEng or PhD degree in a technical field or equivalent practical experience.
• Proven research and engineering experience following the completion of an academic degree.
• Strong knowledge and experience of Python.
• Knowledge of machine learning and/or statistics and/or optimization.
• Strong knowledge of Deep Learning approaches ideally with exposure to Reinforcement Learning.
• Strong knowledge of algorithm design.
  Strong interest evaluating and demonstrating capabilities of AI in impactful applications.

In addition, the following would be an advantage:

• Experience with applying research to products.    
• Working knowledge of Jax / Tensorflow / PyTorch  or similar frameworks.
• Experience with multi-threaded design and parallel/distributed computing.
• Experience with implementing numerical methods and data visualisation.
• Contributions to open source projects.
• Experience with Reinforcement Learning and its applications.