AI Jobs & Internships at Google DeepMind 2026
Google DeepMind was formed in 2023 through the merger of Google Brain and DeepMind, creating the largest AI research organization in the world by headcount and compute access. The combined organization has produced landmark research including AlphaFold (protein structure prediction), AlphaCode (competitive programming), Gemini (Google's flagship AI model), and fundamental advances in reinforcement learning, neuroscience-inspired AI, and AI safety. DeepMind London and Google Brain in Mountain View remain the primary research centers.
AI Roles at Google DeepMind
Research Scientist
Deep Learning Engineer
Reinforcement Learning Researcher
AI Safety Researcher
Research Engineer
Gemini Team Engineer
AI Infrastructure Engineer
Robotics AI Researcher
Work Culture at Google DeepMind
Google DeepMind maintains the academic rigor of a research lab combined with Google's scale and resources. Publication at top venues is encouraged and expected for research roles. The organization has a unique dual identity: DeepMind has a more academic, independent-researcher culture while ex-Brain has a more engineering-heavy product orientation. The merger continues to integrate these cultures. Access to Google's compute infrastructure — the largest AI compute footprint in the world — and cross-organizational collaboration with Google product teams are distinctive advantages.
How to Get a Job at Google DeepMind
- 1.
Strong publication record at top ML venues (NeurIPS, ICML, ICLR, ICAPS) is the primary hiring signal for research roles
- 2.
For research engineer roles, deep proficiency in JAX (Google's primary ML framework) alongside PyTorch is a significant advantage
- 3.
Apply directly through Google's careers portal and ensure your research summary is linked from your application materials
- 4.
Research internships are the most common path to full-time research roles — completing a successful internship with a publishable output is highly effective
- 5.
The interview process typically includes technical ML problem-solving, research presentations, and coding interviews — prepare for all three tracks