Research Engineer, Discovery 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 creative culture and work environment where long-term ambitious research can flourish. The Discovery team combines the best techniques from deep learning, reinforcement learning, meta learning, planning and large models and builds general-purpose agents that discover new knowledge through interaction. We’ve been recently applying these techniques successfully in domains like chess, maths and code. 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 years!

About Us

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.

The role

We are looking for Research Engineers to expand our team at Google DeepMind. As a Research Engineer you will work directly on a wide range of research projects, in collaboration with Research Scientists and Software Engineers. You will apply your engineering and research skills to accelerate research progress through developing prototypes, scaling up algorithms, overcoming technical obstacles, and designing, running, and analysing experiments.

Key responsibilities include:

• Develop research or product prototypes, generating research ideas and collaboratively iterating on their improvement, e.g. by reading and reproducing existing papers, identifying and applying key insights in new contexts, or combining them in novel ways.
• Perform and analyze experiments, and scale up experimentally successful algorithms.
• Build tools and infrastructure in support of research projects, e.g. by surveying the technical landscape, identifying and deploying suitable existing tools, or designing new solutions.
• Act as a bridge between research and engineering, bringing engineering expertise into research projects and research experience into engineering of tools and frameworks.
• Collaborate and communicate ideas, plans and outcomes (orally and in writing) within projects and with adjacent teams, aligning work and timelines with affected teams, sharing insights and reviewing others’ work to achieve milestones.
• Champion engineering standard methodologies within and around the team, e.g. by improving workflows, promoting code reviews, mentoring on code readability, etc.
• Propose direction and advise on projects according to your individual experience and expertise.
• Proactively share your individual skills and knowledge, and collaboratively upskill adjacent engineers and researchers.

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:

• Strong software engineering fundamentals, including fluency in Python and/or C++.
• Experience of ML/scientific libraries such as JAX, PyTorch, TensorFlow, NumPy, …
• Knowledge of mathematics, statistics and machine learning concepts needed to understand research papers and processes in the field.
• Ability to collaborate and communicate technical ideas effectively with colleagues, e.g. through discussions, whiteboard sessions, written documentation, and presentations.

In addition, the following would be an advantage:

• Machine learning and research experience in industry, academia and personal projects, whether in computer science or other fields such as physics, computational biology, or mathematics.
• Experience with Deep RL research
• Experience with machine learning at scale; understanding of multi-accelerator multi-host distributed computation for large models.
• Experience with large scale system design.