Are you passionate about building advanced AI models and turning them into real world scientific impact? Do you thrive at the intersection of machine learning, robotics, and computational chemistry? This is a unique opportunity to join a rapidly growing deep tech company driving the future of molecular discovery. We are looking for an experienced Senior or Staff Machine Learning Scientist to lead projects spanning generative chemistry models, retrosynthesis planning, computer vision, and agentic workflows, all within an environment where your models are immediately tested on a fully automated synthesis platform.
The Employer
Our client is an ambitious Series B deep tech company redefining the way molecules, drugs, and materials are created. By combining AI, robotics, and the world’s largest chemical programming database, they have built an integrated platform that accelerates chemical discovery in ways traditional drug development cannot match. With cutting-edge robotic facilities in the UK and a hybrid team in San Francisco, they offer a dynamic, collaborative culture where innovation moves at unprecedented speed.
Qualifications & Experience
- PhD or equivalent in Machine Learning, Computer Science, Statistics, Physics, or related field.
- 5+ years applied ML experience (Senior) or 8+ years with proven leadership (Staff).
- Expertise in deep learning (PyTorch or JAX), with breadth across two or more of generative models, computer vision, search/planning, or agentic systems.
- Proven ability to take ML models from prototype to production on cloud or HPC environments.
- Strong Python skills and experience designing reproducible pipelines and rigorous benchmarks.
Responsibilities
In this role, you will:
- Develop generative and foundation chemistry models for molecular design.
- Advance retrosynthesis and synthesis-aware ML using reaction databases and robotic data.
- Apply computer vision for real-time feedback from robotic systems.
- Build agentic workflows to automate design–make–test loops.
- Productionize models into reusable toolkits, ensuring best practices in reproducibility and evaluation.
- Mentor junior scientists and, at Staff level, set cross-platform technical direction.
Skills / Technical Competencies
- ML frameworks: PyTorch or JAX.
- Advanced ML areas: generative models, graph/equivariant networks, CNNs, ViTs, MCTS, RL.
- Software practices: pipeline automation, CI/CD, experiment tracking.
- Cloud & HPC: distributed jobs on AWS/GCP/Azure.
Nice to Haves
- Knowledge of retrosynthesis ML, CASP, and cheminformatics tools (RDKit, OpenEye).
- Experience in active learning, Bayesian optimization, or uncertainty quantification.
- Familiarity with drug discovery or materials design workflows.
- Contributions to open-source projects or peer-reviewed publications.