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Chemify Ltd

Staff CADD Scientist

San Francisco, CA · On-site +1

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 ...

Deep Origin

VP/SVP of Drug Discovery

Boston, MA · On-site

We are not building incremental QSAR tools. We are building foundational infrastructure for predictive toxicology in the age of AI, systems biology, and large-scale computation. About the Role We are ...

Deep Origin

VP/SVP of Drug Discovery

San Francisco, CA · On-site

We are not building incremental QSAR tools. We are building foundational infrastructure for predictive toxicology in the age of AI, systems biology, and large-scale computation. About the Role We are ...

Medline

Principal Toxicologist

Northfield, IL

$152K - $229K/yr

Conduct and interpret chemical characterization (e.g., E&L studies, ISO 10993-18, ISO 18562), SAR/QSAR modeling, and guide biocompatibility testing under GLP requirements. * Serve as a subject matter ...

Eurofins

Computational Chemist

Saint Charles, MO · On-site +1

Experience with ligand-based design approaches (e.g., QSAR, similarity searching, conformational analysis) * Knowledge of free energy methods (e.g., FEP, MM-GBSA) and their practical application ...

Medline

Principal Toxicologist

Northfield, IL · On-site +1

$152K - $229K/yr

Conduct and interpret chemical characterization (e.g., E&L studies, ISO 10993-18, ISO 18562), SAR/QSAR modeling, and guide biocompatibility testing under GLP requirements. * Serve as a subject matter ...

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How much do qsar jobs pay per hour?

As of Jun 8, 2026, the average hourly pay for qsar in the United States is $28.47, according to ZipRecruiter salary data. Most workers in this role earn between $20.19 and $30.77 per hour, depending on experience, location, and employer.

What are QSARs?

QSAR stands for Quantitative Structure-Activity Relationship. It refers to computational models used to predict the biological activity or chemical properties of molecules based on their chemical structure. By analyzing patterns between molecular features and observed outcomes, QSAR models help scientists in drug discovery, toxicology, and environmental chemistry to screen compounds more efficiently. These methods reduce the need for extensive laboratory testing and support decision-making in chemical and pharmaceutical industries.

What are the key skills and qualifications needed to thrive as a QSAR (Quantitative Structure-Activity Relationship) Scientist, and why are they important?

To thrive as a QSAR Scientist, you need strong expertise in cheminformatics, computational chemistry, and statistical modeling, usually supported by an advanced degree in chemistry, bioinformatics, or a related field. Familiarity with tools such as Python, R, molecular modeling software, and QSAR-specific platforms, as well as experience with relevant databases, is typically required. Analytical thinking, attention to detail, and effective communication are essential soft skills for interpreting results and collaborating with multidisciplinary teams. These skills are crucial for developing reliable predictive models that advance drug discovery and chemical safety assessments.

What are some common challenges faced by QSAR (Quantitative Structure-Activity Relationship) scientists when working with large chemical datasets?

QSAR scientists often handle large and complex chemical datasets, which can present challenges such as data quality issues, missing values, and chemical structure inconsistencies. Managing and preprocessing this data to create reliable predictive models requires attention to detail and strong problem-solving skills. Additionally, collaborating with interdisciplinary teams—such as chemists, biologists, and data analysts—is essential to interpret results accurately and develop models that are both robust and relevant to real-world applications.

What is the difference between Qsar vs Chemist?

AspectQsarChemist
Required CredentialsDegree in chemistry, pharmacology, or related field; often requires experience in data analysisDegree in chemistry or related field; may require licensure or certification for certain roles
Work EnvironmentResearch labs, pharmaceutical companies, or biotech firms focusing on data modelingLaboratories, manufacturing plants, or research institutions conducting experiments and analysis
Industry UsagePrimarily in drug discovery, environmental science, and chemical research for predictive modelingInvolved in product development, quality control, and research within chemical and pharmaceutical industries

Qsar specialists focus on developing predictive models using chemical data, often working with computational tools. Chemists perform experimental work, analyze substances, and develop new products. While both roles require chemistry knowledge, Qsar roles emphasize data analysis and modeling, whereas chemists focus on hands-on experimentation and synthesis.

More about Qsar jobs
What are the most commonly searched types of Qsar jobs? The most popular types of Qsar jobs are:
What job categories do people searching Qsar jobs look for? The top searched job categories for Qsar jobs are:
Qsar jobs near you
Infographic showing various Qsar job openings in the United States as of May 2026, with employment types broken down into 100% Full Time. Highlights an 84% Physical, 3% Hybrid, and 13% Remote job distribution, with an average salary of $59,222 per year, or $28.5 per hour.

Staff CADD Scientist

Chemify Ltd

San Francisco, CA • On-site, Remote

Full-time

Posted 26 days 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