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In Drug Discovery Jobs (NOW HIRING)

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Scientist, Drug Discovery

Palo Alto, CA · On-site

Headquartered in Palo Alto, CA, we have built a proprietary closed-loop discovery engine - comprising our Disease Signature Atlas, Drug-Gene Atlas, and Conductor AI platform - that integrates single ...

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How much do in drug discovery jobs pay per hour?

As of Aug 16, 2026, the average hourly pay for in drug discovery in the United States is $61.42, according to ZipRecruiter salary data. Most workers in this role earn between $42.55 and $73.80 per hour, depending on experience, location, and employer.

What is the difference between In Drug Discovery vs In Pharmacology?

AspectIn Drug DiscoveryIn Pharmacology
Required CredentialsBachelor's or Master's in Chemistry, Biology, or related fields; lab experienceBachelor's or Master's in Pharmacology, Biology, or related fields; research experience
Work EnvironmentLaboratories, research facilities, collaborative teamsLaboratories, clinical settings, research institutions
Employer & Industry UsagePharmaceutical companies, biotech firms, research institutesPharmaceutical companies, healthcare organizations, research institutes

In Drug Discovery focuses on identifying and developing new drug candidates through laboratory research and screening. Pharmacologists study how drugs interact with biological systems, often working on understanding drug effects and mechanisms. While both roles require scientific backgrounds and lab work, drug discovery emphasizes the creation of new compounds, whereas pharmacology centers on understanding existing drugs' actions.

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Infographic showing various In Drug Discovery job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 77% Full Time, 19% Part Time, 2% Contract, and 1% Nights. Highlights an 97% Physical, 1% Hybrid, and 2% Remote job distribution, with an average salary of $127,745 per year, or $61.4 per hour.

Director Machine Learning, Drug Discovery Analytics

Revolution Medicines

Redwood City, CA • Hybrid

Full-time

Posted 11 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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