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How much do ai machine learning drug discovery jobs pay per year?

As of Aug 23, 2026, the average yearly pay for ai machine learning drug discovery in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

What is AI machine learning in drug discovery?

AI machine learning in drug discovery refers to the use of artificial intelligence algorithms and computational models to identify, design, and develop new pharmaceutical compounds more efficiently. By analyzing large datasets of chemical and biological information, machine learning can predict how potential drugs will interact with targets in the body, speeding up the early stages of drug development. This approach helps researchers identify promising drug candidates, optimize their properties, and reduce the time and cost involved in bringing new medications to market.

How does an AI machine learning professional in drug discovery typically collaborate with interdisciplinary teams during a project?

In drug discovery, AI and machine learning professionals regularly work alongside chemists, biologists, data scientists, and clinical researchers. Collaboration often involves translating complex biological or chemical data into machine learning models, discussing requirements with domain experts, and iterating on model outputs to ensure scientific relevance. Effective communication is essential, as team members rely on the AI expert to explain model findings, address data limitations, and suggest actionable insights for experimental validation. This interdisciplinary approach fosters innovation and accelerates the drug development process.

What are the key skills and qualifications needed to thrive as an AI machine learning drug discovery professional, and why are they important?

To thrive in AI Machine Learning Drug Discovery, you need a solid background in computational biology, chemistry, machine learning algorithms, and typically an advanced degree (PhD or MSc) in a related field. Expertise with programming languages such as Python or R, experience using deep learning frameworks (like TensorFlow or PyTorch), and familiarity with cheminformatics and bioinformatics tools are essential. Strong analytical thinking, problem-solving abilities, and effective collaboration skills set outstanding professionals apart in this field. These skills are crucial for developing innovative solutions, accelerating drug discovery pipelines, and working effectively within interdisciplinary teams.
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Ai Machine Learning Drug Discovery jobs near you
Infographic showing various Ai Machine Learning Drug Discovery job openings in the United States as of August 2026, with employment types broken down into 3% Internship, 78% Full Time, 3% Part Time, and 16% Contract. Highlights an 86% In-person, and 14% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.

Director Machine Learning, Drug Discovery Analytics

Revolution Medicines

Redwood City, CA • Hybrid

Full-time

Posted 18 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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