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Google DeepMind
Research Engineer, Responsible Engineering
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Google DeepMind
Research Engineer, Responsible Engineering
London
In office
Description
We are looking for REs to join the responsible engineering team in Google DeepMind’s Foundational Research Team.
Powerful next-generation AI systems are in the pipeline; system builders and operators will need foundational theory, methods, and practices to make them safer, more trustworthy, and easier to align with overall benefit to the public. Responsible AI, which seeks those traits, is a growing, hugely interdisciplinary field that needs contributors from all backgrounds: join us and help evolve both AI and AI engineering towards a richer and more conscientious proficiency.
The role
As part of the Responsible Engineering team, you will work with your teammates and project collaborators on research and systems for machine learning research and its applications.
You’ll learn/extend, adapt, and operationalise relevant interdisciplinary knowledge (sociotechnical AI, HCI, ML interpretability, public policy, the list grows constantly) toward AI projects — at all stages of the AI development pipeline but with a particular focus on earlier, more exploratory research. In all projects, you’ll mix proactive imagination and creativity with our team’s lengthy practical experience, both to predict and mitigate new and familiar harms, and to see and exploit opportunities for more socially beneficial AI systems.
Key responsibilities
As a research engineer, you will bring a combination of engineering and research skills to the task of advancing GDM’s mission. Depending on the requirements of a project, you will be responsible for some of the following:
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 analyse 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 best practices 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.
Skills and experience that will help you, or that you will be able to grow in this role are:
Python-based ML/scientific libraries such as JAX, PyTorch, TensorFlow, NumPy, …
Research literacy (mathematics and statistics, understanding of research papers, …)
Large-scale system design, distributed systems
Effective collaboration and communication (discussion, presentation, technical and research writing, whiteboard sessions, …)
Familiarity with contemporary social impacts and risks/opportunities (short- and long-term) of real-world AI system deployment.
Gathering data and feedback on AI systems from human raters.
Responsible AI is a new and broad field, making it difficult to name additional specific skill items. However it manifests concretely for you, your general interest in topics like the following can be of use:
How deployment of new technologies affects large social systems; how impactful technologies affect and are affected by (and are) culture.
The professional responsibilities of AI system developers and deployers.
Large-scale AI systems and the natural environment.
Making interdisciplinary topics accessible to technical audiences.
Curiosity about the internal workings of large neural networks and other machine learning models.
Role tech stack
Google DeepMind
Research Engineer, Responsible Engineering
London
This role has expired