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.
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