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

Our research team at DeepMind focuses on pushing the boundaries of Machine Learning and Artificial Intelligence theory & practice in order to contribute to our mission. This fundamental research includes but is not limited to deep neural models, reinforcement learning algorithms and biologically-inspired models with the overall goal of building powerful general-purpose learning algorithms.

We have created a passionate and engaging culture, combining academic and product-led environments, to provide a supportive balance of structure and flexibility. Our approach encourages collaboration across all groups within the Research team, leading to ambitious creativity and the scope for innovative research breakthroughs.

The Open-Endedness team is developing open-ended systems at scale that never stop producing new, interesting, and complex artefacts as well as increasingly capable, adaptive, self-driven, self-improving, general-purpose, multi-modal agents. We believe this will ultimately be critical for achieving DeepMind's mission of solving intelligence. We draw upon a range of technical fields from foundation models, world models, multi-agent systems to evolutionary computation and self-improvement methods. Current team members include Aditi Mavalankar, Jack Parker-Holder, Jimmy Shi, Michael Dennis, and Tim Rocktäschel.

About Us

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.

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. Our Research Engineers are pivotal to the development and ongoing improvement of our research through the computational implementation of our latest theoretical work.

Research Engineers make many different vital contributions to our engineering infrastructure and research programme. Typical work will include:

Providing software design and programming expertise to research projects - pairing closely with Research Scientists to better engineer and implement the latest algorithmic ideas.

Digest and understand complex research papers, theory and methodology, with an ability to write algorithms from scratch. This role is pivotal in training, iterating and improving the performance of our systems.

Own, report and present engineering developments and research experimental results to both the immediate and broader research teams. status and results clearly and efficiently both internally and externally, verbally and in writing.

Architect and implement software libraries to allow our research to improve and scale.

Implement and evaluate algorithms - acting as a key contributor to the development and iteration throughout the research cycle.

Write high quality code (Python and/or C++) to be shared within a research group or more broadly.

Encouraging engineering excellence through mentoring and reviewing.

Engineering robust and scalable open-ended AI systems.

Developing research code that is both flexible for experimentation and has high readability.

Using state-of-the-art compute and ML infrastructure, and pushing it to its limits.

What we offer

A variety of complex problems to work on, with the opportunity to learn constantly through experimentation

Access to a team of leading researchers, engineers, and problem solvers to learn from - with the opportunity to contribute your own thinking and specialist knowledge to add to our mission

Constant learning, training and development opportunities, from technical courses to being a better presenter - design it to work best for you!

Access to leading technology, ever-evolving tech stacks and Google-scale systems to allow your work to flourish

An opportunity to be the first RE in the team, and help to define coding practices and system architectures.

About You

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

MSc/MEng in a technical field or equivalent practical experience.

Proven experience, either in industry or a research lab, working on complex ML problems and engineering workflows

Strong knowledge and experience of Python and/or C++

Experience using machine learning frameworks such as TensorFlow, JAX or Pytorch.

Proven knowledge of machine learning and/or statistics..

Experience with the workflow of a machine learning project, from idea prototyping to analysis and debugging.

Knowledge of distributed systems, parallel computing, HPC, CUDA programming, file systems.

Experience working with accelerators like GPUs and TPUs.

In addition, the following would be an advantage:

A passion for AI, in particular open-ended learning systems!

PhD in an area related to machine learning.

Experience with parallel/distributed computing.

Closing date - Wednesday 5th July 2023