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How much do machine learning chemistry jobs pay per hour?

As of Aug 19, 2026, the average hourly pay for machine learning chemistry in the United States is $22.26, according to ZipRecruiter salary data. Most workers in this role earn between $18.27 and $24.52 per hour, depending on experience, location, and employer.

What is a machine learning chemistry?

A Machine Learning Chemistry job involves using artificial intelligence techniques to analyze chemical data, model molecular behaviors, and accelerate discoveries in chemistry-related fields. Professionals in this role develop and apply machine learning algorithms to predict chemical properties, optimize reactions, and assist in drug design, material science, and other applications. They typically work in pharmaceuticals, materials science, or environmental chemistry, collaborating with chemists, data scientists, and engineers to solve complex chemical problems efficiently.

What does a machine learning chemistry do?

Professionals in Machine Learning Chemistry often work on projects such as developing predictive models for chemical property analysis, optimizing molecular structures, or advancing drug discovery through data-driven methods. Daily tasks may include data preprocessing, building and training machine learning models, validating results, and interpreting outcomes in collaboration with experimental chemists. Teamwork is common, with regular interactions between chemistry researchers, data scientists, and software engineers. This structure allows for iterative feedback and ensures that computational models align with practical lab needs. Continuous learning and adaptation are also key, as both the chemistry and machine learning fields are rapidly evolving.

What are the key skills and qualifications needed to thrive in machine learning chemistry?

To thrive in a Machine Learning Chemistry role, you need a solid background in chemistry, expertise in data science and machine learning algorithms, and typically an advanced degree in chemistry, computer science, or a related field. Familiarity with programming languages like Python or R and experience working with cheminformatics tools and machine learning frameworks (such as TensorFlow or scikit-learn) are essential. Strong analytical thinking, problem-solving abilities, and effective communication skills enable professionals to bridge the gap between computational work and experimental research teams. These competencies are crucial for developing innovative solutions in chemical research and ensuring successful collaboration across interdisciplinary teams.

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Machine Learning Chemistry jobs near you
Infographic showing various Machine Learning Chemistry job openings in the United States as of August 2026, with employment types broken down into 5% Internship, 70% Full Time, 20% Part Time, and 5% Nights. Highlights an 100% In-person job distribution, with an average salary of $46,292 per year, or $22.3 per hour.

Director Machine Learning, Drug Discovery Analytics

Revolution Medicines

Redwood City, CA • Hybrid

Full-time

Posted 14 days ago

Job description

The Opportunity:

We are seeking a Director Machine Learning to lead the development of advanced machine learning approaches that accelerate small-molecule drug discovery. This role sits at the intersection of data science, chemistry, and biology, transforming complex scientific datasets into predictive models that guide target discovery, compound design, and translational hypotheses.

Working closely with experimental scientists, the Director ML will develop cutting-edge modeling approaches that integrate chemical, biological, and phenotypic data with their team. The successful candidate will play a key role in advancing a data-driven discovery strategy by designing predictive models, deploying innovative algorithms, and translating insights into actionable decisions that improve the speed and success of the discovery of medicines for patients with RAS-driven cancers.

Key responsibilities include:

Scientific Leadership:

  • Provide hands-on scientific leadership in drug discovery analytics spanning Identify opportunities where AI and advanced analytics can meaningfully improve scientific decision-making

  • Managing, coaching and mentoring scientists across the function in order to develop their skills and build RevMed's organizational capabilities

  • Define and lead machine learning strategies that accelerate early-stage drug discovery.

Model Development:

  • Develop predictive models for:

    • Compound activity, selectivity, ADME/Tox, and developability properties

    • Target engagement, mechanism-of-action, and phenotypic datasets

Cross-Functional Collaboration:

  • Work with biologists to interpret complex experimental datasets and generate mechanistic hypotheses.

  • Collaborate with data scientists and engineers and ML engineers to deploy models into scalable discovery workflows.

Required Skills, Experience and Education:

  • PhD in machine learning, computational chemistry, computational biology, computer science, or a related quantitative discipline.

  • 8+ years experience applying machine learning or advanced analytics to scientific problems.

  • Demonstrated experience working with chemical or biological datasets in drug discovery or related domains.

  • Strong expertise in:

    • Python-based ML ecosystems (PyTorch, TensorFlow, scikit-learn)

    • Data analysis and scientific computing (NumPy, Pandas)

    • Deep learning and representation learning techniques

  • Evidence of successful coaching, mentorship and development of both individuals and teams in order to build long-term organizational capability

  • Passion for scientific innovation and a relentless commitment to improving patient outcomes.

Preferred Skills:

  • Proven track record of applying advanced AI/ML approaches (deep learning, generative modeling, structure-based ML) to drug discovery or related life sciences domains.

  • Experience with cheminformatics or bioinformatics toolkits is highly desirable.

  • Familiarity with cloud computing and scalable ML workflows is a plus

  • Ability to work at the interface of computational and experimental science. 

    #LI-Hybrid  #LI-LN1

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