AI Research Scientist

Flex is a spinout from the Dror Lab at Stanford using cutting-edge computational tools to design breakthrough small molecules that target cryptic pockets and allosteric sites on challenging drug targets. To enable this, we invented a new computational technology based on diffusion models that is orders of magnitude more efficient than traditional physics-based simulations or virtual screening. Our technology simultaneously designs binders and explores the conformational space of the target receptor structure.

Backed by a tier 1 Silicon Valley investor, the company has already initiated two discovery programs on membrane protein targets where there remains a large unmet medical need for an oral small molecule.

We are looking for an AI research scientist specializing in Structural Biology to join our team and advance our generative modeling efforts for drug discovery. In this role, you will develop and apply diffusion models and other cutting-edge machine learning approaches to model protein-ligand interactions. You will leverage these techniques to uncover novel druggable sites, including cryptic pockets and allosteric sites on challenging targets. This cross-disciplinary position bridges research and product development and is ideal for someone with a strong background in machine learning and computational biology/chemistry who thrives in a fast-paced startup environment.

• Develop and optimize state-of-the-art generative models (especially diffusion models) for protein structure modeling and small-molecule design.
• Design, implement, and run computational experiments to evaluate the performance and robustness of ML models.
• Curate and standardize datasets and use physics-based approaches to generate synthetic training data.
• Contribute to our computational platform by writing robust, production-quality code (Python, PyTorch) and developing scalable ML pipelines for model training and analysis.

• PhD in technical subject with major engineering component and exposure to AI/ML, or BSc, MSc and 5+ years of specific experience working on AI/ML model development and application.
• Expertise in machine learning for biology/chemistry, with hands-on experience in generative models and familiarity with related approaches (e.g., AlphaFold, graph neural networks, transformers).
• Proficiency in Python programming and deep learning frameworks (especially PyTorch), including experience building and optimizing ML model training pipelines.
• Strong understanding of ML theory and applications.

• Experience with generative models for structural biology (protein structure prediction, protein design).
• Experience deploying or scaling computational workflows on high-performance computing clusters or cloud platforms.
• Familiarity with drug discovery pipelines or structure-based drug design principles.
• Familiarity with molecular modeling and cheminformatics libraries (e.g., RDKit, Rosetta) for structure-based drug design tasks.

• Starting salary range is $200-250k cash with additional equity-based compensation. To determine starting pay, we consider multiple job-related factors including a candidate's skills, education and experience, market demand, business needs, and internal parity. We may also adjust this range in the future based on market data.
• This role is eligible for participation in our Annual Performance Bonus Plan (based on company targets by role level and annual company performance) and our Equity Incentive Plan.

• If you are interested in applying for this role, please contact us at alex.powers@flextherapeutics.com and sylvain.gariel@flextherapeutics.com