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

As of Jul 13, 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 July 2026, with employment types broken down into 75% Full Time, 22% Part Time, and 3% Contract. Highlights an 66% Physical, 3% Hybrid, and 31% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.
Principal Machine Learning Scientist, Drug Discovery Analytics

Principal Machine Learning Scientist, Drug Discovery Analytics

Revolution Medicines

Redwood City, CA • Hybrid

Other

Re-posted 19 days ago

Job description

The Opportunity:

We are seeking a Principal Machine Learning Scientist 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 Principal ML Scientist will develop cutting-edge modeling approaches that integrate chemical, biological, and phenotypic data. 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:

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

  • Identify opportunities where AI and advanced analytics can meaningfully improve scientific decision-making.

  • Drive the adoption of innovative modeling approaches within multidisciplinary discovery teams.

Model Development:

  • Develop predictive models for:

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

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

Apply modern ML techniques such as:

  • Graph neural networks.

  • Deep learning for molecular representation.

  • Generative chemistry models.

  • Active learning frameworks for experimental design.

Cross-Functional Collaboration:

  • Partner with medicinal chemists to guide compound design and optimization.

  • 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.

Data Integration:

  • Integrate heterogeneous datasets including:

  • Chemical structure and screening data.

  • Imaging and phenotypic screening data.

  • Structural biology and molecular simulation outputs.

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.

  • Strong understanding of early-stage drug discovery workflows.

  • Ability to translate biological or chemical questions into computational frameworks and predictive models.

  • Proven ability to communicate complex computational insights to.

  • 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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