Staff CADD Scientist

Staff CADD Scientist Chemify Ltd San Diego, CA Job Details 16 hours ago Qualifications Drug discovery Deep learning

AI Python Full Job Description About Chemify:

Chemify is revolutionising chemistry. We are creating a future where the synthesis of previously unimaginable molecules, drugs, and materials is instantly accessible. By combining AI, robotics, and the world's largest continually expanding database of chemical programs, we are accelerating chemical discovery to improve quality of life and extend the reach of humanity. Our Chemifarm facility in Glasgow operates a growing fleet of advanced robotic systems that automate synthesis, optimisation, and library generation. This gives our computational scientists something rare: a direct, high-throughput bridge from in silico design to physically synthesised molecules, closing the designmaketest loop at a pace conventional drug discovery organisations cannot match.

Location:

San Francisco (hybrid) or fully remote from Boston /

San Diego Travel:

Regular travel to our Glasgow HQ / Chemifarm The Role We are seeking a Staff CADD Scientist to drive computer-aided drug design on Chemify's commercial programmes and computational platform. You will sit at the centre of a cross-disciplinary team computational chemists, in-house and partner medicinal chemists, AI researchers, data engineers, and automation scientists and shape how structure, simulation, and machine learning translate into molecules we actually make. Your work sits at the interface between Chemify's platform and our commercial partners' drug discovery programmes. You will design and prioritise molecules for synthesis, work directly with partner chemists on medicinal-chemistry strategy turning computational proposals into physically-made compounds. If you are energised by solving complex scientific problems at the intersection of chemistry, physics, and AI and by seeing your designs synthesised and tested within days rather than months we'd love to welcome you to our team. Key Responsibilities Own the computational design approach on assigned programmes, from hit discovery through lead optimisation; partner with in-house and customer chemists on MPO and translate SAR into actionable hypotheses across DMTL cycles. Deploy and advance methods across the CADD stack docking, pharmacophore, shape and 3D-similarity, MD, FEP, QSAR modelling choosing the right blend of physics- and ML-based approaches for each programmes. Communicate reasoning, trade-offs, and recommendations to partner chemists and project leads. Help productionise CADD methods into a reproducible, API-first toolkit; partner with Infrastructure on cost-effective GPU/HPC workflows. Mentor computational chemists and junior CADD scientists; partner with the Head of Advanced Machine Learning on hiring and growth; act as the scientific interface with customers on commercial projects. Represent Chemify's CADD capability externally publications, conferences, and partner engagements where appropriate. About You You are a rare hybrid: a deeply credible computational chemist who is equally comfortable reasoning protein-ligand interactions and shipping code that runs in production. You care about getting real molecules made, not only writing elegant methods.

We expect you to bring:

PhD (or equivalent experience) in Computational Chemistry, Structural Biology, Biophysics, Physics, or a closely related field, plus 8+ years of hands-on CADD experience in small-molecule drug discovery including owning the computational strategy on active programmes. Strong grounding in both structure- and ligand-based drug design proteinligand biophysics on one side, and pharmacophore, shape, and SAR-driven design on the other with hands-on use of standard CADD stack (e.g. MOE, PyMOL, Open

MM / GROMACS / AMBER

). Familiarity with core drug discovery and medicinal chemistry principles translating diverse assay readouts into design hypotheses and a clear understanding of pharmacological principles to keep CADD output biologically relevant. Working knowledge of modern deep learning for molecular design (GNNs, generative models, property prediction), and a clear sense of when these complement traditional CADD methods rather than replace them. Strong Python and at least one core cheminformatics toolkit (e.g. RDKit, OpenEye); real experience inside the drug-discovery loop (SAR, MPO, DMTL cycles, lead optimisation, library enumeration); comfort with GPU-accelerated simulation and cloud/HPC workflows. The ability to present computational reasoning to working chemists and partner scientists and a track record of technical leadership beyond your own projects. Beneficial Skills Hands-on experience with free energy perturbation (FEP+, OpenFE, or equivalent) in a production drug-discovery setting. Practical use of generative chemistry methods (diffusion, autoregressive, RL-based design), including a clear-eyed view of their failure modes. Familiarity with active learning, iterative DMTL design loops, and Bayesian optimisation applied to molecular design. Experience building or integrating CADD tooling into API-first platforms (FastAPI, Docker, CI/CD), and proficiency in

C/C++ / CUDA

for high-performance computational chemistry. A visible track record in the field peer-reviewed publications, open-source contributions, or public projects that demonstrate your judgement on real CADD problems. Why Join Chemify?

Impact:

You will directly shape the molecules Chemify designs and makes at a company uniquely positioned to close the designmaketest loop through automated chemical synthesis at scale.

Autonomy:

Reporting to the Head of Advanced Machine Learning, you will own CADD strategy on your programmes, choose the methods and tools, and have meaningful influence over the computational platform that supports every project at Chemify.

Ambition:

We are a Series B deep-tech company investing in world-class infrastructure and tackling problems at the frontier of AI, robotics, and chemistry. You will have the resources, the data, and the mandate to do CADD in a way that isn't possible elsewhere.