Google DeepMind

Official guidance

Google DeepMind is Google’s AI research and model-building organization, behind Gemini, Veo, AlphaFold and other systems. Its careers page describes Research Engineers (implementing, optimizing and scaling models and building distributed-computing infrastructure), Software Engineers (reliable, efficient software at scale), Research Scientists, and product, program and responsibility roles.

Relevant roles: General software engineering, ML engineering, Research-oriented ML implementation, Training / inference infrastructure, Research engineering. Domains: Algorithms, ML & applied AI, AI infrastructure, AI research, Research methods, Interview skills.

What the sources establish

Google DeepMind’s careers page and its linked PDF interview guide describe a typical four-stage process (a 30-minute recruiter call and possibly a hiring-manager interview, two or three skills interviews, final interviews with team leads and leadership, then a decision), a competency-based approach with STAR, mostly virtual interviews, a 4 to 10 week timeline, and a rule against AI tools in live interviews and interview tasks unless told otherwise. Not established: what the skills interviews test for a given role (coding, ML theory, implementation, research discussion), languages or frameworks, whether take-home tasks are used, and level differences.

Careers at Google DeepMind
Official
Google DeepMind. Undated page; read 2026-10-05. Read in full for this page.
Scope: All Google DeepMind roles; the page says exact steps differ by role
Documented by this source
- Typical stages: initial interviews, skills interviews, final interviews, then decision and offer; invited candidates receive role-specific preparation information
- Initial interviews: a 30-minute introductory call with a recruiter, possibly followed by an interview with the hiring manager about specific capabilities
- Skills interviews: two or three further calls evaluating the competencies and skills required for the position, where you meet potential peers
- Final interviews: team leads and leadership, including the potential manager, focusing on core skills through the lens of team goals, culture, mission and values
- Role descriptions: Research Engineers implement and optimize frontier models and build distributed-computing infrastructure; Software Engineers build reliable, efficient software at scale; Research Scientists usually hold a PhD and drive research
Outside or AI tools, according to this source: Not addressed on this page; see the linked interview guide. Follow the instructions in your own assessment invitation.
- The stage list is a typical overview; the technical content of skills interviews is not specified.
Open source(opens in a new tab)Link responded, 5 Oct 2026
Interviewing at Google DeepMind (PDF guide)
Official
Google DeepMind. Undated PDF (the server reports a last modification on 2025-11-28); read 2026-10-05. Read in full for this page.
Scope: Google DeepMind candidates generally; internships are run by the Google Recruiting team, not this process
Documented by this source
- Four stages (initial, skills, final, decision and offer), with role-specific preparation information for invited candidates
- Many interviews take a competency-based approach; STAR is recommended, and all candidates are assessed against clearly defined criteria
- Interviews are mostly virtual over Google Meet with screen sharing; candidates sign a standard NDA before interviewing
- Preparation suggestions: learn about Google DeepMind’s research and models, review the job description, practice with Google’s Interview Warmup tool, and watch a coding-preparation video for roles that involve coding
- Tips: think out loud, say when you do not know something, explain your reasoning, and use data to show impact
- The process takes 4 to 10 weeks depending on the role; Google DeepMind and Google interview processes are run by different teams
Outside or AI tools, according to this source: You may use AI to prepare, but unless told otherwise, do not use AI tools during live interviews or interview tasks. Follow the instructions in your own assessment invitation.
- Served from Google DeepMind’s cloud-storage media bucket and linked from the official careers page, rather than hosted on deepmind.google itself.
- Does not list technical topics, coding languages or the format of skills interviews.
Open source(opens in a new tab)Link responded, 5 Oct 2026

Our preparation recommendation

This is our suggestion based on the role and the sources above. It is not a statement by Google DeepMind.

Our recommendation, not Google DeepMind guidance: the sources say little about technical content, so prepare from the role description. Research Engineer candidates should combine ML fundamentals, implementation and training-loop fluency, and scaling knowledge; Software Engineer candidates should weight coding and systems; research roles should add experimental design and paper discussion. Prepare competency-based STAR answers for every role, since the guide recommends that structure.

Pattern recognition and mixed practiceNot started
The guide points coding roles to coding-interview preparation; broad pattern practice is a reasonable base.
Neural networks, backpropagation and numerical stabilityNot started
Research Engineers are described as needing a deep understanding of machine learning and deep learning.
Transformers, attention and language-model objectivesNot started
Frontier model work such as Gemini builds on transformer architectures.
Training loops, optimizers and model debuggingNot started
Research Engineers are described as experimentalists who build systems to test and evaluate new ideas.
PyTorch and autograd fluencyNot started
Implementation fluency with an autodiff framework; confirm which framework the team uses.
Data, tensor and pipeline parallelism and collectivesNot started
Research Engineers build infrastructure for distributed computing and scale models.
Experimental design: controls, baselines and ablationsNot started
For research roles and Research Engineers who design experiments to test ideas.
Behavioral stories and values-based questionsNot started
Many interviews are competency-based, and the guide recommends STAR.
Project deep divesNot started
The guide asks you to discuss successes, lessons and impact backed by data.

Confirm with your recruiter

What each skills interview tests for your role (coding, ML theory, implementation, systems, research discussion) and how long each lasts.
The level and team, and whether the final interviews include technical content.
Which programming language and ML framework you may use in coding or implementation interviews.
Whether any interview task or take-home exists, its time limit, and whether AI tools are explicitly allowed for it (the guide’s default is no).
Logistics: Google Meet with screen sharing, NDA signing, time zones and accommodations.
If you are also interviewing at Google or another Alphabet company, how the parallel processes are coordinated.

Limitations of this page

Neither source describes the technical content of interviews for any specific role.
Internships and student researcher positions follow different processes not covered here.