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.
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 mission of our team is to build a learning system that operates at unprecedented scale, while efficiently adapting to any new task and data. The team works at the intersection of research and engineering, advancing the state of the art in several machine learning areas, from large-scale learning, to distributed optimization, transfer learning, auto-ML, continual learning, among others. The aim is to produce transformative research artefacts for the research community and to impact real world applications in a variety of domains.
Our team’s approach is focused and creative, with a highly supportive and collaborative culture.
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.
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
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:
- PhD in an area related to machine learning.
- Experience with parallel/distributed computing.
- A passion for AI!