Research Scientist

Logical Intelligence; San Francisco (CA); In loco; USD 140.000 - 190.000; Tempo pieno

Descrizione del lavoro

Logical Intelligence seeks to accelerate materials discovery by building AI systems that propose and evaluate candidate molecules. You will work with chemists, physicists, and ML engineers to turn ideas into reusable models tested against lab data.

You’ll develop energy-based and diffusion models, and collaborate with partner labs to ensure synthesis and measurement yield informative feedback for continual learning.

Competenze

• PhD or Master’s with hands-on ML in scientific problems.
• Experience building ML models for real scientific applications.
• Excellent scientific writing and communication in interdisciplinary teams.

Mansioni

• Build and improve AI systems behind the discovery loop, including candidate generation and screening.
• Develop energy-based and diffusion models to search chemical space efficiently.
• Own model evaluation with benchmarks, uncertainty calibration, and prospective lab tests.
• Compare AI predictions with lab measurements and implement model improvements.
• Design experiments with partner labs to maximize information per synthesis/test cycle.

Conoscenze

Machine learning

Computational chemistry

Physics background

PhD or MSc

Interdisciplinary collaboration

Hands-on ML in science

Formazione

PhD

Masters degree

Strumenti

Energy-based models

Diffusion models

Generative modeling

Molecular ML

Descrizione del lavoro

At Logical Intelligence, our mission is to accelerate the discovery of materials that make the next generation of technology possible, from light emitters and semiconductors to batteries, electric vehicles, and the chips behind AI and robotics. Large language models alone are not enough to get there. Discovering new materials requires an understanding of chemistry and physics to guide the search, and energy-based models let us build that understanding directly into our discovery loop. We work closely with industry and lab partners to shorten the path from research to real products.

As a Research Scientist, AI for Materials Discovery on our material discovery team, you will help turn that idea into new molecules. You will build the AI systems that propose and evaluate candidates, and work with our partner labs so that every synthesis and measurement teaches the models something. You will join a highly interdisciplinary team of chemists, physicists, and machine learning scientists and engineers, where the biggest impact comes from working together.

What You’ll Do

• Build and improve the AI systems behind our discovery loop, from candidate generation and screening to selecting which molecules to test next.
• Develop energy-based models that combine learned representations with physical and chemical knowledge to search chemical space more efficiently.
• Own model evaluation, including benchmarks on held-out chemical families, accuracy against both quantum-chemistry calculations and experiment, calibration of uncertainty estimates, prospective tests where the model predicts before the lab measures, and tracking across model versions.
• Compare AI predictions with measurements, investigate disagreements, and turn what you learn into concrete model changes, in collaboration with AI researchers and engineers.
• Design experiments with our partner labs so that each round of synthesis and testing is as informative as possible.

You Will Thrive in This Role If You Have

• A background in machine learning, computational chemistry, physics, or a related field, with a PhD, or a Master’s plus relevant industry experience, and hands-on experience building ML models for real scientific problems.
• Familiarity with energy-based models, diffusion or score-based models, or similar generative approaches, and an interest in going deeper.
• Knowledge of chemistry, or a genuine interest in learning it alongside our chemists.
• 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, and comfort working in an interdisciplinary team.

Especially Strong Candidates May Also 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