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Computational Scientist Rdkit Jobs (NOW HIRING)

Vilya

Machine Learning Engineer

Seattle, WA

$154K - $174K/yr

Collaborate with and support the computational scientists at Vilya to help build a world-class ... Familiarity with basic cheminformatics libraries and tools (RDKit, OpenBabel, PyMol, etc.) * An ...

Eli Lilly and Company

CADD Postdoctoral Scientist

Boston, MA · On-site

$58K - $123.20K/yr

PhD in Computational Chemistry, Computer Science, or a related field . * Strong background in CADD ... Proficiency in Python and familiarity with cheminformatics tools (e.g., RDKit ). * Proficiency in ...

Lilly

CADD Postdoctoral Scientist

Indianapolis, IN

$58K - $123.20K/yr

PhD in Computational Chemistry, Computer Science, or a related field . * Strong background in CADD ... Proficiency in Python and familiarity with cheminformatics tools (e.g., RDKit ). * Proficiency in ...

Lilly

CADD Postdoctoral Scientist

Boston, MA

$58K - $123.20K/yr

PhD in Computational Chemistry, Computer Science, or a related field . * Strong background in CADD ... Proficiency in Python and familiarity with cheminformatics tools (e.g., RDKit ). * Proficiency in ...

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Computational Scientist Rdkit information

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$50.5K

$111.3K

$137.5K

How much do computational scientist rdkit jobs pay per year?

As of May 31, 2026, the average yearly pay for computational scientist rdkit in the United States is $111,343.00, according to ZipRecruiter salary data. Most workers in this role earn between $94,500.00 and $137,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Computational Scientist working with 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.

How does a Computational Scientist working with 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 is a Computational Scientist specializing in 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 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.

More about Computational Scientist Rdkit jobs
What cities are hiring for Computational Scientist Rdkit jobs? Cities with the most Computational Scientist Rdkit job openings:
What states have the most Computational Scientist Rdkit jobs? States with the most job openings for Computational Scientist Rdkit jobs include:
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Computational Scientist Rdkit jobs near you
Infographic showing various Computational Scientist Rdkit job openings in the United States as of May 2026, with employment types broken down into 86% Full Time, 10% Part Time, and 4% Contract. Highlights an 73% Physical, 2% Hybrid, and 25% Remote job distribution, with an average salary of $111,343 per year, or $53.5 per hour.

Senior / Staff Machine Learning Scientist

Chemify Ltd

San Francisco, CA • On-site

Full-time

Posted 17 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)
Travel: Regular travel to our Glasgow HQ / Chemifarm
The Role
We are seeking a Senior / Staff Machine Learning Scientist to work across the breadth of Chemify's platform - generative models for chemistry, search and planning for retrosynthesis, computer vision for telemetry from our robotic systems, and agentic workflows that tie it all together. You will partner with computational chemists, CADD scientists, software engineers, and hardware engineers, and apply AI/ML to build the next generation of Chemify's platform.
What sets this role apart is the combination of breadth of ML problems - generative chemistry, vision, search, agents - paired with a robotic platform that turns your models into physical experiments.
If working across a wide range of hard ML problems on a real-world platform sounds like the right shape of job for you, we'd love to welcome you to our team.
Key Responsibilities
  • Build generative and foundation chemistry models for molecular design.
  • Advance retrosynthesis and synthesis-aware ML by leveraging Chemify's reaction database and robot-execution data.
  • Apply computer vision to transform robot telemetry into models that monitor process state and feedback into experimental control.
  • Prototype agentic workflows that orchestrate models, tools, and the platform - closing loops between proposal, execution, observation, and learning.
  • Productionise models into a reproducible, API-first toolkit; partner with Infrastructure on GPU training and HPC; maintain high standards of ML best practices, including rigorous evaluation, benchmarks, and reproducibility.
  • Mentor junior ML scientists, partner with the Head of Advanced Machine Learning on hiring and growth, and represent Chemify's AI/ML capability externally.
  • (Staff level) Set technical direction across the AI/ML stack; lead cross-cutting initiatives spanning chemistry models, retrosynthesis, vision, and agents.

About You
You are an experienced ML scientist who is equally comfortable training models and shipping the code that other people end up building on. You care about whether your model changes a real decision - not just whether it beats a benchmark. You're at home moving across problem types, from generative models to vision to search.
We expect you to bring:
  • PhD or equivalent experience in Machine Learning, Computer Science, Statistics, Physics, or a related quantitative field - 5+ years (Senior) or 8+ years (Staff) of hands-on applied ML experience, including production-grade work.
  • Deep familiarity with modern deep learning stack (PyTorch or JAX), and breadth across at least two of: generative models (diffusion, autoregressive, flow-based), graph and equivariant networks, vision (CNNs, ViTs, multimodal LLMs), search and planning (MCTS, A*), or agentic / RL systems.
  • Experience taking ML from prototype to production: reproducible pipelines, distributed jobs, and batch workflows on cloud (AWS / GCP / Azure) or HPC.
  • Strong scientific computing instincts: clean Python, careful experiment design, leakage-aware splits, and rigorous benchmarks.
  • Clear communication with non-ML scientists and engineers and a willingness to pick up new domains (you don't need to know chemistry on day one).
  • (Staff level) A track record of technical leadership: mentoring, setting standards, and influencing scientific and technical direction beyond your own projects.

Beneficial Skills
  • Practical experience with active learning, Bayesian optimisation, conformal prediction, or uncertainty quantification in iterative real-world loops.
  • Familiarity with retrosynthesis ML, computer-aided synthesis planning (CASP), or reaction-condition / yield prediction.
  • Working knowledge of how ML fits into a drug-discovery or materials-design workflow, plus familiarity with cheminformatics tooling (e.g. RDKit, OpenEye) - or willingness to pick these up.
  • MLOps fluency: experiment tracking, data versioning, model serving, and observability of deployed models.
  • A visible track record in the field - peer-reviewed publications, open-source contributions, or public projects that demonstrate your judgement on real ML problems.

Why Join Chemify?
Impact:
Your models will directly shape what Chemify's robotic platform proposes, plans, and observes - at a company uniquely positioned to close the loop between design and physical experiment.
Autonomy:
Reporting to the Head of Advanced Machine Learning, you will work across the AI/ML problems with the most impact, and have meaningful influence over the direction of our AI/ML capability.
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 and the mandate to do AI/ML in a way that isn't possible elsewhere - across chemistry, vision, search, and autonomous systems on the same platform.

Apply