Retrosynthesis Researcher, Machine Learning

Retrosynthesis Researcher, Machine Learning

New York

Schrödinger seeks a Retrosynthesis Researcher in Machine Learning (ML) to join us in our mission to transform the discovery of therapeutics and materials. Schrödinger pioneers a physics‑based software platform that enables discovery of high‑quality, novel molecules for drug development and materials applications more rapidly and at lower cost compared to traditional methods. The software platform is used by biopharmaceutical and industrial companies, academic institutions, and government laboratories worldwide.

Our multidisciplinary drug discovery team also leverages the platform to advance collaborative programs and our own pipeline of novel therapeutics to address unmet medical needs.

As a member of our Machine Learning team, you’ll work at the forefront of computational chemistry and AI, contributing to high‑impact research with real‑world applications in small molecule drug discovery and materials science.

• An ML expert who has applied AI tools to chemical reaction prediction or retrosynthesis (e.g., reaction templates, template‑free approaches) and understands organic synthesis and reaction mechanisms
• An experienced user of cheminformatics tools (e.g., RDKit, Open Babel)
• A proficient Python programmer familiar with ML tools like PyTorch, Tensor Flow, and JAX
• An excellent problem‑solver comfortable working collaboratively in a multidisciplinary research environment

• Develop and implement AI/ML models (e.g., graph neural networks, transformer‑based models) for retrosynthetic pathway prediction
• Apply deep learning techniques to predict reaction outcomes, optimize reaction conditions, and identify novel synthetic routes
• Curate and manage reaction datasets from literature, patents, and proprietary sources to train and validate predictive models
• Integrate retrosynthesis tools with cheminformatics platforms and molecular modeling software
• Collaborate with synthetic chemists to experimentally validate predicted retrosynthetic routes and optimize laboratory workflows
• Contribute to scholarly publications in high‑impact journals and represent the research group in conferences and workshops

• PhD in Chemistry, Computational Chemistry, Cheminformatics, or a related field
• A solid publication record that demonstrates expertise in retrosynthesis algorithms and computational chemistry

• Familiarity with chemical reaction databases (e.g., Reaxys, USPTO, Pistachio)
• Knowledge of computer‑aided synthesis planning (CASP) tools and retrosynthetic analysis software (e.g., Ai Zynth Finder , ASKCOS, IBM RXN)
• A background in graph‑based learning, attention mechanisms, and transformer architectures applied to chemical data
• Familiarity with reaction condition prediction and reaction yield optimization
• Experience with Schrödinger Suite and Live Design
• Experience with de novo design and generative machine learning methods
• Experience with cloud computing and/or high‑performance computing (HPC) resources
• Exposure to quantum chemistry (DFT) is a plus

Schrödinger understands it’s people that make a company great. Because of this, we’re prepared to offer a competitive salary, equity‑based compensation, and a wide range of benefits that include healthcare (with dental and vision), a 401(k), pre‑tax commuter benefits, a flexible work schedule, and a parental leave program. We have regular catered meals in the office, a relaxed but engaged company culture, and over a month of paid vacation time.

New York houses our largest office, but teams are located worldwide.

Estimated base salary range: $120,000 – $145,000. Actual compensation depends on experience, education, degrees held, market data, and business needs.

As an equal opportunity employer, Schrödinger hires outstanding individuals into every position in the company. We place the highest value on creating a safe environment where employees can grow and contribute, and refuse to discriminate on the basis of race, color, religious belief, sex, age, disability, national origin, citizenship status, marital status, partnership status, caregiver status, sexual and reproductive health decisions, gender identity or expression, sexual orientation, or any other protected characteristic.