Data Research Engineer, Data Team

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Snapshot

We’re looking for a number of Data Research Engineers to join the newly formed Data Team at DeepMind. We are open to candidates at a variety of seniority levels, and it is a great opportunity to join in the very early days of the team!

There is also the option to have this position scoped at 4 days a week (32 hours) for candidates who wish to work a reduced schedule. If you would like to select this option, you can select the part-time option in the application questions.

About Us

Artificial Intelligence could be one of humanity’s most useful inventions. At DeepMind, we’re a team of scientists, engineers, machine learning specialists and more, working together to advance the state of the art in Artificial Intelligence. Our mission is to solve intelligence to advance science and benefit humanity.

Data is playing an increasingly crucial role in research innovation, with improvements in data quality largely responsible for some of the most significant research breakthroughs in recent years. The Data Team is a newly formed team responsible for the sourcing, acquisition, generation, processing, and management of data to efficiently fuel DeepMind’s research. Engineers on the team will embed in research projects, working in direct collaboration with world-leading AI researchers, as well as other partner teams (e.g, program management, partnerships, safety, ethics, governance, and compliance), to ensure our AI models are powered by data that is fit for purpose.

The Role

We’re looking for a number of Data Research Engineers to join our Data team. As a member of this team you will help guide and execute the strategy for data collection and innovation of new data generation methods, working closely with research efforts to produce datasets targeted to current needs.

Data is central to the advancement of AI research, and as a Data Research Engineer you will play a key role in improving the range and quality of data used in research at DeepMind. You will be a core contributor on pioneering research projects, and your day-to-day work will involve collaborating closely with other researchers to ensure we optimally harness the power of data.

The responsibilities you could have include:

• Building scalable end-to-end dataset generation pipelines, and making the resulting datasets available for training large models
• Conducting deep exploratory analyses to inform new data collection and processing methods
• Developing new, scalable methods to extract, clean, and filter data
• Ingesting new datasets, partnering with both external and internal sources when necessary
• Designing and implementing performant data-loading code to enable efficient training and evaluation of models
• Researching ways in which models can make better use of data, and ways we can use data to more effectively evaluate our models
• Running large-scale experiments with human annotators/subjects
• Evaluating data that comes in from experiments and communicating feedback to annotators and other researchers
• Controlling data quality and building content moderation
• Collaborating with our Safety, Ethics, (https://deepmind.com/safety-and-ethics) and Governance teams to ensure our data are developed and utilised safely and ethically

In this role, you could be ingesting and curating large-scale datasets, or interfacing and experimenting with human-in-the-loop data, so we are looking for people who are interested in either of these problem spaces. If you are excited by one (or both) of these prospects, then do apply!

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

• Strong software engineering skills, including fluency in Python
• Good understanding of algorithm design and a creative approach to problem solving
• Demonstrable practical interest in data engineering
• Proficiency exploring, analysing, visualising, cleaning, and uncovering the underlying trends in large datasets of (messy) real-world data
• Solid understanding of statistics, linear algebra, and basic ML concepts
• Experience working with databases and data analysis tools/libraries (e.g. Pandas, SQL)
• Attention to detail and curious mindset— when faced with ambiguity your instinct is to dig into the data to understand what is really going on, and not take performance metrics at face value

In addition, the following would be an advantage: 

• Experience processing large datasets in parallelised/distributed settings
• Experience applying machine learning methods to real-world data
• Proficiency in C++
• An interest in understanding and mitigating systemic bias in datasets

If you don’t think you embody all the above criteria, please still seriously consider applying. This role (and therefore the requirements) is broad, combining aspects of data engineering, data science, and research, so we are open to candidates from all these backgrounds (and more). We’d be excited to discuss how you see yourself contributing to the role, and in particular the opportunities to learn and grow on the job.

Competitive salary applies