Research Scientist, Provenance, DeepMind

On-site in London·Added today (last checked 10 Nov)

Google

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Requirements

Minimum qualifications:

PhD in Machine Learning or equivalent research/industry experience.
Experience in scripting languages and pipelining tools.
Experience in programming.
Quantitative experience in maths and statistics.
Published work in a ML conference on the subject of robustness or image or video generation or classification.

Preferred qualifications:

Experience in applying machine learning techniques to problems surrounding robust and trustworthy deployments of models.
Experience with image, audio or text watermarking.
Demonstrated success in delivering high quality research impact.
Passion for AI.

The role

Our team accelerates research in strategic projects that enable trustworthy, robust and reliable machine learning with a group of research scientists and engineers on a mission-driven team. Together, we will apply ML and other computational techniques to a wide range of challenging problems. A public example of recent work is SynthID, and we are actively pursuing other exciting projects across many disciplines.

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 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.

As a Research Scientist in Strategic Initiatives, you will use your machine learning expertise to collaborate with domain experts and other machine learning scientists within our strategic initiatives programs. Domains may include, but are not limited to, machine learning robustness, the provenance of synthetic media, and the trustworthiness of data.

Artificial intelligence will be one of humanity's most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.

We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.

Responsibilities

Engage with existing research efforts in the Strategic Initiatives Program to solve key challenges using your broad machine learning experience.
Collaborate with researchers and machine learning engineers to identify and develop novel machine learning approaches to solve meaningful strategic problems.
Report and present research findings and developments (including status and results) clearly and efficiently both internally and externally, verbally and in writing.
Suggest and engage in team collaborations to meet research goals for the wider science programme.

Hiring process

4 steps3 interviewsa task to do6–8 weeks in all

Apply online
Assessments
Online test · Not every role
You may be asked to take the Google Hiring Assessment (workstyle skills) and, for some roles, role-related assessments such as a coding exercise.
Recruiter conversations
With a recruiter · Phone call · 1–2 sessions
One or two shorter conversations assessing key skills, sharing information about the organisation and team, and asking about your preferences such as location.
Project work
In your own time · Not every role
For some roles, a small project before interviews: a case study, writing or code samples.
Interviews
With a rotating cast of Googlers
A panel of structured interviews, over video or in person, with a rotating cast of Googlers; open-ended role-related questions scored against the same rubric for every candidate, no brainteasers.
Decision and offer · With a recruiter

Structured interviews: every candidate for a role is scored against the same rubric; no brainteasers.
AI tools are not permitted during interviews.
For some roles, pre-selected interviews happen virtually before final interviews onsite.