Machine Learning Scientist -- Drug Discovery (Small Molecules)About the OpportunitySmall molecule drug discovery is one of the most high-impact and intellectually demanding challenges in modern machine learning. Traditional pharmaceutical development is costly, slow, and heavily reliant on trial-and-error, often taking over a decade and billions of dollars to bring a single new drug to market. This presents a massive opportunity for machine learning to transform the space by enhancing prediction accuracy, enabling generative molecular design, and drastically shortening development cycles.
This role offers the opportunity to work at the intersection of deep learning, generative modeling, and computational chemistry, with the goal of radically accelerating the discovery of novel therapeutics. You will be part of a team that has spent years pushing the boundaries of ML for drug development, collaborating with top-tier pharma clients, and publishing impactful research. Your ImpactAs a Machine Learning Scientist, you will:Design and implement novel machine learning architectures (e.g., diffusion models, graph neural networks, Transformers) tailored to the optimization of small molecule properties.
Drive research-first development: test new algorithmic approaches, quantify uncertainty, and contribute to the creation of a robust and generalizable ML platform for molecular design. Collaborate with interdisciplinary experts across chemistry, pharmacology, and software engineering to bring research concepts into practical applications. Contribute to the scientific roadmap of an ML-native drug discovery platform, influencing both technical direction and applied use cases.
Publish in top-tier ML venues (NeurIPS, ICML, ICLR), where appropriate, and participate in broader scientific dialogue. What We're Looking ForPh.
D. in Computer Science, Applied Mathematics, Statistics, Physics, or a closely related quantitative discipline. Strong publication record, preferably as a first author, in top ML conferences (e.g., NeurIPS, ICML, ICLR). Deep understanding of modern machine learning, including:Generative models (e.g., diffusion, VAEs, GANs)Transformers and attention mechanisms
Graph neural networks
Uncertainty modeling and Bayesian optimization
Fluency in Python and deep learning frameworks such as PyTorch.2+ years of experience writing production-quality research code (version control, code review, unit testing, etc.). Demonstrated ability to work autonomously on open-ended research problems. No prior experience in chemistry or biology is required --- domain knowledge will be provided through close collaboration with internal experts.
Why This Role? Work on one of the most ambitious and meaningful applications of AI: using machine learning to solve real-world problems in drug development.
Join a lean, elite team of researchers and scientists who value curiosity, autonomy, and technical depth. Rapid iteration cycle: datasets are small enough to train meaningful models in days, not months --- enabling true research velocity. Contribute to real-world drug discovery projects that have the potential to impact human health at scale.
If you're passionate about applying your ML expertise to meaningful, cross-disciplinary challenges --- and want to work at the frontier of science and technology --- this is a rare and rewarding opportunity.
