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Snapshot
Artificial Intelligence could be one of humanity’s most useful inventions. At Google Deep Mind, 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.
About Us
Accelerate 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, you 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 exciting projects in the space of data trustworthiness.
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.
The Role
As a Research Engineer 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, multimedia GenAI pre-training, post-training and inference with an emphasis on data influence and trustworthiness.
Key responsibilities:
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Engage with existing research efforts in the Strategic Initiatives Program to solve key challenges using your broad machine learning experience.
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Collaborate with researchers and machine learning engineers to identify and develop novel machine learning approaches to solve meaningful strategic problems.
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Engage with product teams to drive the deployment of our research.
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Report and present research findings and developments (including status and results) clearly and efficiently both internally and externally, verbally and in writing.
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Suggest and engage in team collaborations to meet research goals for the wider Science programme.
About You
In order to set you up for success as a Research Engineer at Google Deep Mind, we look for the following skills and experience:
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MSc/Bachelor in Computer Science, or ML practical experience
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Experience with GenAI models for text or multimodal (image, audio, video) across the training and inference lifecycle
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Experience with embeddings and autoraters
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Quantitative skills in maths and statistics.
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Programming experience across common scripting languages and pipelining tools.
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A real passion for AI!
In addition, the following would be an advantage:
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PhD demonstrating significant advances in machine learning
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Experience in robust and large-scale deployment and inference of those models
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Demonstrated success in delivering high quality research impact.
At Google Deep Mind, 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.
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About Google DeepMind

Google DeepMind
AcquiredDeepMind Technologies Limited, trading as Google DeepMind or simply DeepMind, is a British-American artificial intelligence research laboratory which serves as a subsidiary of Alphabet Inc.
1,001-5,000
Employees
London
Headquarters
Reviews
3.8
10 reviews
Work Life Balance
3.8
Compensation
4.2
Culture
3.5
Career
4.0
Management
2.8
68%
Recommend to a Friend
Pros
Smart and brilliant colleagues
Good compensation and benefits
Work flexibility and remote options
Cons
Poor management and leadership issues
Bureaucracy and slow processes
Constantly changing priorities and goals
Interview Experience
5 interviews
Difficulty
3.0
/ 5
Duration
21-35 weeks
Offer Rate
60%
Experience
Positive 60%
Neutral 40%
Negative 0%
Interview Process
1
Application Review
2
Phone Screen/Online Assessment
3
Technical Interview
4
Team Matching Interview
5
Offer
Common Questions
Coding/Algorithm
Technical Knowledge
Behavioral/STAR
Research Experience
System Design
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