AI Research Scientist/ Research Engineer: Materials Discovery

The role

You will build and improve AI systems for a material discovery loop. You will develop energy-based models and work with partner labs to design experiments and evaluate model predictions.

What you will do

• Build and improve AI systems for candidate generation, screening, and selection.
• Develop energy-based models combining learned representations with physical and chemical knowledge.
• Own model evaluation including benchmarks, accuracy against calculations and experiment, and uncertainty calibration.
• Compare AI predictions with measurements and investigate disagreements.
• Design experiments with partner labs to ensure informative synthesis and testing.

Required qualifications

• PhD in machine learning, computational chemistry, physics, or a related field, or a Master’s plus relevant industry experience.
• Hands-on experience building ML models for real scientific problems.
• Familiarity with energy-based models, diffusion or score-based models, or similar generative approaches.
• Knowledge of chemistry, or a genuine interest in learning it.
• Experience designing evaluations and experiments that correlate with downstream performance.
• A strong record of impactful research shown through publications, patents, open-source software, or methods used in real discovery programs.
• Excellent scientific writing and communication.
• Comfort working in an interdisciplinary team.

Nice to have

• Hands-on research experience with energy-based models, or with methods that combine learning with search and optimization.
• Experience with molecular ML, such as property prediction, generative molecular design, ML interatomic potentials, or quantum-chemistry workflows (e.g. DFT).
• Experience with closed-loop design using real lab feedback

About the company

Logical Intelligence aims to accelerate the discovery of materials for technologies like semiconductors, batteries, and electric vehicles. They work with industry and lab partners to shorten the path from research to real products.