Material Science Research Engineer, DeepMind

At Google, research-focused Software Engineers are embedded throughout the company, allowing them to setup large-scale tests and deploy promising ideas quickly and broadly. Ideas may come from internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world. From creating experiments and prototyping implementations to designing new architectures, engineers work on real-world problems including artificial intelligence, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more. But you stay connected to your research roots as an active contributor to the wider research community by partnering with universities and publishing papers.
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.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $147000 - $211000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google .

Plan and perform rapid prototyping of machine learning techniques applied to problems in science.

Undertake exploratory analysis to inform experimentation and research directions.

Make improvements to model architectures and training procedures of machine learning models.

Implement tools, libraries, and frameworks to speed up and enable new research.

Report and present software developments, experimental results, and data analysis clearly and efficiently.

Minimum qualifications:

Bachelor's degree in Computer Science, Electrical Engineering, Mathematics, Statistics, a related technical field, or equivalent practical experience.

2 years of experience applying software engineering principles in a scientific research environment.

Experience working with linear algebra, calculus and statistics.

Experience performing data exploration or data analysis across datasets.

Experience with JAX, PyTorch, or TensorFlow.

Preferred qualifications:

Master’s degree or PhD in Computer Science, Electrical Engineering, Science, Mathematics, or equivalent practical experience.

Specific domain expertise in areas like inorganic chemistry, solid-state physics, or materials synthesis.

Experience applying modern deep learning architectures (e.g., transformers, diffusion models) to chemistry or materials science issues (e.g., ML force fields).

Experience running large-scale scientific simulations (e.g., molecular dynamics, computational chemistry simulations, etc.) on Cloud or HPC clusters.

Experience developing custom LLM agents or tool-using systems.

Experience with concurrent and distributed software algorithms and architectures.

Bachelor's degree in Computer Science, Electrical Engineering, Mathematics, Statistics, a related technical field, or equivalent practical experience.

2 years of experience applying software engineering principles in a scientific research environment.

Experience working with linear algebra, calculus and statistics.

Experience performing data exploration or data analysis across datasets.

Experience with JAX, PyTorch, or TensorFlow.