Are you an experienced Staff Computational Chemist with a passion for designing real molecules and seeing them made within days, not months? Do you thrive at the intersection of CADD, AI-driven design, and automation? This is a rare opportunity to join a pioneering deep-tech company where your work directly shapes the future of drug discovery. We are looking for a Staff CADD Scientist to take ownership of computational strategy, integrating cutting-edge simulation and machine learning into a platform that closes the design–make–test loop at unprecedented speed. The Employer Our client is a Series B deep-tech company transforming chemical discovery by combining AI, robotics, and the largest ever-growing database of chemical programs. Their state-of-the-art robotic facility in Glasgow provides a high-throughput bridge from in silico models to physically synthesised compounds, giving scientists a unique ability to turn design into reality faster than traditional pharma methods. With a San Francisco base (hybrid) and UK site, this is an ambitious, impact-driven environment at the forefront of AI and chemistry. Qualifications & Experience:
- PhD (or equivalent) in Computational Chemistry, Structural Biology, Biophysics, Physics, or related discipline.
- 8+ years hands-on experience in CADD for small-molecule drug discovery, including strategy ownership and delivery.
- Expertise across structure- and ligand-based design plus proficiency with MOE, OpenMM/GROMACS/AMBER, PyMOL.
- Strong Python skills and experience with cheminformatics toolkits (RDKit, OpenEye), GPU-accelerated workflows, and cloud/HPC.
- Familiarity with modern ML for molecular design (GNNs, generative models) and understanding of when they complement traditional methods.
- Proven leadership and experience presenting complex computational reasoning to chemists and partners.
Responsibilities You will:
- Lead computational strategy on drug discovery programmes from hit discovery through lead optimisation.
- Design and prioritise molecules for synthesis; partner with chemists on SAR-driven hypotheses and MPO.
- Apply CADD methods, docking, pharmacophore, shape and 3D similarity, MD, FEP, QSAR, blending physics and ML-based approaches.
- Help develop a reproducible, API-driven computational platform and cost-efficient GPU/HPC pipelines.
- Mentor junior scientists, influence hiring, and represent the business externally across conferences and partnerships.
Skills / Technical Competencies
- Advanced command of ligand- and structure-based design workflows.
- Proficiency with CADD toolkits, molecular dynamics, and QSAR.
- Strong programming in Python, cheminformatics libraries, and experience with production-level pipelines.
- Cloud/HPC workflow management and GPU acceleration.
Nice to Haves:
- Hands-on FEP experience (FEP+, OpenFE).
- Practical use of generative chemistry models and active learning in iterative design loops.
- Experience integrating CADD tools into API-first platforms and MLOps environments.
- Contributions to peer-reviewed publications or open-source projects in CADD.