About the Team
The AQBioSim team develops AI and quantum solutions for computational science, with a near-term focus on drug design. We seek a revolutionary impact on human health, the environment, and the economy. Simulation & Optimization (S&O) Scientists will develop and implement novel physics-based, AI, quantum, and quantum-inspired algorithms for drug design and beyond. Working closely with a focused team of PhDs, MBAs, engineers, and product experts, you will confront hard social problems, make business impacts, and improve lives.
About the Role
As a key member of the AQBioSim team, you will leverage and improve state-of-the-art machine learning techniques in chemistry and biophysics to accelerate novel drug discoveries. This role demands a proactive innovator with exceptional problem-solving skills and a results-driven mindset. You'll work within a multi-disciplinary team, contributing to a collaborative environment free from office politics.
What You’ll Do
- Develop and improve novel deep learning methods (diffusion models, transformers, graph neural networks, etc.) applied to chemistry and drug discovery problems.
- Develop innovative ML methods to predict functional characteristics and properties of molecules and biological systems for rational structure-based drug design.
- Help guide and scope projects with clear deliverables alongside agile teams
- Collaborate closely with multi-disciplinary teams to independently prototype and scale cutting-edge, impactful drug design solutions.
- Generate and evaluate hypotheses to assist design decisions and influence project direction by developing and deploying computational methods and workflows.
- Effectively present and communicate research findings through talks, blog posts, clients, and scientific publications.
About You:
- M.S. with 2-3 years of relevant experience or Ph.D. in computational physics, computational chemistry, computer science, or related field
- Proficient in Python and modern software development practices.
- Experience with ML and deep learning tools (e.g., PyTorch, TensorFlow) and cheminformatics toolkits (e.g., RDKit).
- Knowledge in deploying ML models on cloud platforms (e.g., AWS, GCP, Azure).
- Demonstrated success in applying ML to drug discovery or materials science, with a portfolio of impactful contributions.
- Experience in one or more of the following ML applications to drug discovery or materials science: chemical space exploration, reinforcement learning, active learning, generative methods for chemistry, ML force fields, crystal structure prediction, reaction pathway prediction, QSAR, similarity search, chemical space visualization, ADME ML predictions, PK ML predictions, chemical clustering, knowledge graphs, foundation models
Nice-to-haves
- At least 3-5 years of industry and hands-on experience developing and applying deep learning models to address open molecular design problems in drug discovery.
- Authorship of publications in high-impact peer-reviewed journals or conferences.
- Familiarity with non-ML methods for structure-based drug design. For example, molecular docking, traditional cheminformatics, and molecular dynamics
- Exposure to ML methods in a low-data regime (few-shot or zero-shot learning)
- Familiarity with other data modalities important for drug discovery campaigns, including but not limited to omics data, patient records, and microscopy data.
