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Machine Learning Drug Discovery Postdoc Jobs (NOW HIRING)

Vilya

Machine Learning Engineer

Seattle, WA · On-site

Responsibilities : • Develop and maintain the infrastructure to support machine learning workflows for drug discovery at scale. • Implement and optimize algorithms for data processing, model ...

Vilya

Machine Learning Engineer

Seattle, WA · On-site

$154K - $174K/yr

Develop and maintain the infrastructure to support machine learning workflows for drug discovery at ... scale. * Implement and optimize algorithms for data processing, model training and model deployment.

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Machine Learning Drug Discovery Postdoc information

What is a Machine Learning Drug Discovery Postdoc?

A Machine Learning Drug Discovery Postdoc is a postdoctoral researcher who uses advanced machine learning techniques to accelerate and improve the drug discovery process. They work at the intersection of computational science, biology, and chemistry to develop algorithms that can predict molecular properties, identify potential drug candidates, and optimize compounds. Their research helps pharmaceutical companies and academic labs find effective drugs more efficiently, often reducing the time and cost required for new drug development. Typically, these postdocs collaborate closely with interdisciplinary teams and may also contribute to scientific publications and conferences.

What are some typical challenges faced by a Machine Learning Drug Discovery Postdoc, and how can they be addressed?

As a Machine Learning Drug Discovery Postdoc, one of the main challenges is integrating complex biological data with advanced computational models to generate meaningful insights for drug development. Addressing issues such as data sparsity, heterogeneity, and ensuring model interpretability are common hurdles. Collaborating closely with wet-lab scientists, bioinformaticians, and other computational researchers is essential for validating predictions and translating findings into actionable experiments. Regular communication with interdisciplinary teams and staying updated on the latest computational techniques can help overcome these challenges and drive impactful research.

What are the key skills and qualifications needed to thrive as a Machine Learning Drug Discovery Postdoc, and why are they important?

To thrive as a Machine Learning Drug Discovery Postdoc, you need a strong background in computational biology, machine learning, and chemistry, typically supported by a PhD in a relevant field. Expertise with programming languages (such as Python or R), deep learning frameworks (like TensorFlow or PyTorch), and bioinformatics tools is highly valuable. Strong analytical thinking, collaboration, and effective scientific communication are crucial soft skills for advancing research projects and sharing results. These skills and qualities are essential to drive innovation, interpret complex biological data, and translate computational models into actionable drug discovery insights.
Principal Machine Learning Scientist, Drug Discovery Analytics

Principal Machine Learning Scientist, Drug Discovery Analytics

Revolution Medicines

Redwood City, CA • Hybrid

Other

Posted 13 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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