Machine Learning Research Scientist at well-funded AI materials discovery startup

As a Machine Learning Research Scientist, you will drive the core discovery engine of an autonomous laboratory. You’ll develop novel ML architectures, including GNNs and foundation models, to bridge the gap between simulation and real-world physical experiments. This role offers the unique opportunity to see your research directly accelerate scientific discovery and materials innovation.

Well-funded AI materials discovery startup

• Formulate and prototype novel ML architectures like foundation models and GNNs tailored for complex material representations and physics simulations.
• Design active learning and optimization algorithms to autonomously decide which physical experiments the lab should run to maximize discovery.
• Tackle challenges in representation learning using sparse, high-dimensional data generated from real-world physical experiments to sharpen model predictions.

• Holds a PhD in Computer Science, Machine Learning, Physics, or a related field with a strong record of publishing novel research.
• Possesses hands-on expertise in PyTorch or JAX developing modern architectures such as generative models, Bayesian optimization, or foundation models.
• Demonstrates experience applying machine learning to scientific domains or simulations with a deep curiosity for accelerating discovery through autonomous systems.

• You will work at the intersection of frontier AI and physical labs, creating a closed-loop system for autonomous scientific discovery.
• The company is backed by top-tier VCs and led by world-renowned experts from elite industry labs and prestigious universities.
• Your work will have direct real-world impact by solving fundamental bottlenecks in materials science for EVs, robotics, and clean energy.