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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 the key skills and qualifications needed to thrive as a machine learning drug discovery postdoc?

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.

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 popular job titles related to Machine Learning Drug Discovery Postdoc jobs?

For Machine Learning Drug Discovery Postdoc jobs, the most frequently searched job titles are:

Infographic showing various Machine Learning Drug Discovery Postdoc job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 72% Full Time, 24% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Machine Learning Engineer - Drug Discovery

South San Francisco, CA • On-site

Astrix Inc
Recruiting and Staffing Services • 1 - 5K employees
Learn more about Astrix Incexternal

$60 - $70/hr

Full-time, Contractor

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Pay Rate Low: 60 | Pay Rate High: 70
Our client is a leading biotech company seeking a highly motivated AI/ ML Scientist to join their innovative research organization focused on applying artificial intelligence and machine learning to drug discovery and molecular design.
Title: Machine Learning Scientist - Drug Discovery
Location: Remote - United States (PST preferred)
Schedule: Full-Time, 40 hours/week
Contract Duration: 12 months, with a strong possibility of extension
Employment Type: W-2 + Benefits
Compensation: $60-$70/hour, depending on experience and qualifications
Job Details:
This role will focus on designing, developing, training, and deploying advanced machine learning models and computational engines that support lab-in-the-loop molecular design and optimization. Areas of focus include sequence modeling, molecular structure, conformational ensembles, molecular property prediction, natural language processing, computer vision, and robotics. The successful candidate will work in a highly collaborative, multidisciplinary environment alongside ML scientists, ML engineers, computational scientists, and drug design experts to develop next-generation solutions at the intersection of AI and life sciences.
Key Responsibilities
  • Design, develop, optimize, evaluate, and deploy advanced deep learning models, including large language models, multimodal transformers, and generative AI models.
  • Build and optimize scalable data pipelines supporting machine learning and scientific applications.
  • Optimize model training and inference for performance, scalability, and accuracy using multi-GPU and cloud-based infrastructure.
  • Develop and maintain MLOps workflows covering model deployment, version control, monitoring, reproducibility, and ongoing model performance.
  • Develop machine learning approaches that connect diverse datasets, including genomics, transcriptomics, imaging, molecular, and clinical data.
  • Partner with scientists and engineers across disciplines to translate innovative machine learning methods into practical applications for drug discovery, disease research, and biomedical applications.
  • Independently troubleshoot complex modeling, software, and infrastructure challenges and drive solutions from development through deployment.

Qualifications:
  • B.S., M.S., or Ph.D. in Computer Science, Machine Learning, Computational Biology, Data Science, Statistics, Mathematics, or a related quantitative discipline.
  • Must be authorized to work in the United States without current or future employer sponsorship.
  • 1-5 years of relevant professional experience, including postdoctoral research where applicable.
  • Strong foundation in data structures, algorithms, software engineering, and computational problem solving.
  • Expert-level Python programming skills.
  • Extensive experience with deep learning frameworks such as PyTorch, JAX, or TensorFlow.
  • Strong debugging and software development skills, with the ability to independently diagnose and resolve complex technical issues.
  • Experience with large-scale or distributed model training, such as DDP, Ray, FSDP, or DeepSpeed.
  • Experience with model deployment technologies such as Triton or ONNX.
  • Experience working with cloud and/or GPU computing infrastructure.
  • Hands-on experience with geometric deep learning, molecular cofolding models, neural force fields, or related scientific ML approaches is highly preferred.

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