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Computational Scientist Rdkit Jobs in California

Chemify Ltd

Staff CADD Scientist

San Francisco, CA · On-site +1

This gives our computational scientists something rare: a direct, high-throughput bridge from in ... RDKit, OpenEye); real experience inside the drug-discovery loop (SAR, MPO, DMTL cycles, lead ...

Satomic

Staff Engineer, Chemical Search

San Diego, CA · On-site

The medicinal chemists, computational scientists, and generative-model-driven design teams will ... You will decide what to build versus buy on cheminformatics-specific tooling (RDKit / OpenEye for ...

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Computational Scientist Rdkit information

What is a computational scientist RDKit?

A Computational Scientist specializing in RDKit is a professional who uses computational methods and the RDKit cheminformatics toolkit to analyze and model chemical structures and reactions. They often work in fields like drug discovery, materials science, or chemical engineering, leveraging RDKit for tasks such as molecular fingerprinting, property prediction, virtual screening, and data visualization. Their expertise combines advanced programming skills, a strong understanding of chemistry, and the ability to develop or optimize algorithms for chemical data analysis.

What are the key skills and qualifications needed to thrive as a computational scientist RDKit, and why are they important?

To thrive as a Computational Scientist using RDKit, you need a strong background in cheminformatics, computational chemistry, and programming, typically with an advanced degree in chemistry, bioinformatics, or a related field. Proficiency in Python, familiarity with RDKit libraries, and experience using molecular modeling and data analysis tools are essential. Critical thinking, problem-solving, and effective collaboration are important soft skills for translating scientific questions into computational solutions. These competencies enable accurate molecular data analysis, innovation in research, and successful teamwork in interdisciplinary environments.

What is the difference between Computational Scientist Rdkit vs Computational Chemist?

AspectComputational Scientist RdkitComputational Chemist
Required CredentialsDegree in Chemistry, Bioinformatics, or related; programming skills in Python; familiarity with RDKitDegree in Chemistry, Chemical Engineering, or related; strong background in molecular modeling and programming
Work EnvironmentResearch labs, biotech companies, pharmaceutical firms; focus on software development and data analysisAcademic or industrial labs; focus on experimental design, molecular simulations, and data interpretation
Industry UsageUsed in cheminformatics, drug discovery, and molecular data analysisApplied in pharmaceuticals, materials science, and chemical research

Computational Scientist Rdkit specializes in developing and applying cheminformatics tools using RDKit, often combining programming and data analysis. Computational Chemist focuses on molecular modeling, simulations, and chemical research. While both roles require chemistry knowledge and programming skills, the Computational Scientist Rdkit role emphasizes software development and data processing, whereas the Computational Chemist emphasizes experimental and theoretical chemistry applications.

How does a computational scientist RDKit typically collaborate with chemists and software engineers on research projects?

Computational Scientists specializing in RDKit often work closely with chemists to translate scientific questions into computational workflows, such as molecule property prediction or virtual screening. They also collaborate with software engineers to integrate RDKit functionalities into larger platforms or to optimize code for performance and scalability. Effective communication and project management are essential, as these interdisciplinary teams rely on regular meetings, shared documentation, and iterative feedback to ensure research goals are met efficiently. This collaborative environment not only fosters scientific innovation but also provides opportunities to learn from experts in related fields.
What are popular job titles related to Computational Scientist Rdkit jobs in California? For Computational Scientist Rdkit jobs in California, the most frequently searched job titles are:
What job categories do people searching Computational Scientist Rdkit jobs in California look for? The top searched job categories for Computational Scientist Rdkit jobs in California are:
What cities in California are hiring for Computational Scientist Rdkit jobs? Cities in California with the most Computational Scientist Rdkit job openings:
Computational Scientist Rdkit jobs near you
Infographic showing various Computational Scientist Rdkit job openings in California as of August 2026, with employment types broken down into 1% Internship, 1% As Needed, 84% Full Time, 9% Part Time, 2% Temporary, and 3% Contract. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution.

Staff CADD Scientist

Chemify Ltd

San Francisco, CA • On-site, Remote

Full-time

Re-posted 7 hours ago

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 design-make-test 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 - protein-ligand 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, OpenMM / 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 design-make-test 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.

Apply