DeepMind
Research Engineer, Materials Science
About this role
Google DeepMind seeks a Research Engineer to accelerate materials science discovery by combining AI, computational simulation, and automated experimentation. You'll collaborate with an interdisciplinary team to prototype machine learning solutions, build research infrastructure, and explore novel applications of AI to materials problems.
What you'll do
- Prototype machine learning techniques for scientific problems in materials research
- Conduct exploratory analysis to guide experimentation and research directions
- Optimize model architectures and training procedures for ML systems
- Develop tools, libraries, and frameworks to enable new research
- Present software developments, experimental results, and data analysis findings
- Collaborate with internal and external scientific domain experts
What they're looking for
- Software engineering in research environments
- Linear algebra, calculus, and statistics
- Machine learning libraries (JAX, PyTorch, TensorFlow, NumPy, Pandas)
- Large-scale data exploration and visualization
- Deep learning architectures (transformers, diffusion models)
- Materials science or computational chemistry domain knowledge
- Distributed computing and HPC systems
- LLM agents or tool-using systems development
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DeepMind
DeepMind develops advanced AI systems across multiple domains, including multimodal models for understanding human communication and detecting manipulated media, as well as AI applications in materials science discovery. The company is hiring research engineers and researchers to build foundational AI capabilities, create defenses against digital misinformation, and accelerate scientific discovery through machine learning and computational innovation.
- Website
- deepmind.google.com
Likely interview questions
- Can you describe a time you applied machine learning to a scientific problem? What was the challenge, and how did you validate your approach?
- Tell us about your experience with ML frameworks like PyTorch, JAX, or TensorFlow. How have you used them in a research setting?