Research Engineer, Materials Science

Stack

Responsibilities

• ▹Rapid prototyping of machine learning techniques applied to scientific problems
• ▹Undertake exploratory analysis to inform experimentation and research directions
• ▹Improve model architectures and training procedures
• ▹Implement tools, libraries, and frameworks to speed up new research
• ▹Report and present software developments, experimental results, and data analysis
• ▹Collaborate with internal and external scientific domain experts

Requirements

• ▹Degree in computer science, electrical engineering, science, mathematics or equivalent experience
• ▹Experience applying software engineering principles in a scientific research environment
• ▹Knowledge of linear algebra, calculus, and statistics at least at first-year university level
• ▹Experience exploring, analyzing, and visualizing large, noisy datasets
• ▹Experience with Jax, PyTorch, TensorFlow, NumPy, Pandas, or similar ML/scientific libraries

Nice to have

• ▹Domain expertise in inorganic chemistry, solid-state physics, or materials synthesis
• ▹Applying modern deep learning architectures (transformers, diffusion models) to chemistry or materials science
• ▹Running large-scale scientific simulations (molecular dynamics, computational chemistry) on Cloud or HPC
• ▹Developing custom LLM agents or tool-using systems
• ▹Experience with concurrent and distributed software algorithms
• ▹Master's or PhD in computer science, electrical engineering, science, mathematics, or equivalent

Soft skills

What we offer

• ▹Bonus and equity
• ▹US base salary range: $141,000-$202,000 (plus bonus, equity, benefits)

About the company

Google DeepMind runs a pioneering materials research program combining AI, computational simulation, and automated experimentation to accelerate discovery of new functional materials (e.g. DM21, GNoME projects).