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Ai Machine Learning Drug Discovery Jobs (NOW HIRING)

1910

AI Research Scientist I

Boston, MA

Learn how a molecule processes through the drug discovery process * Work cross-functionally with scientific colleagues, being a subject matter expert in how AI and Machine Learning can be used to ...

1910

AI Research Scientist III

Boston, MA · On-site

... Machine Learning can be used to answer cheminformatics and bioinformatics questions • Keep up-to-date on cutting-edge research in the AI for drug discovery space • Manage junior AI Research ...

Elevance Health

AI Machine Learning Scientist

Tampa, FL · On-site +1

AI Machine Learning Scientist Location: This role requires associates to be in-office 1 day per week, fostering collaboration and connectivity, while providing flexibility to support productivity and ...

Masimo

Engineer II, AI/Machine Learning

Irvine, CA · On-site

$120K - $150K/yr

The AI/Machine Learning Engineer II will be part of the R&D team at Masimo with focus on design and ... The key focus will be in analyzing data from different sources to discover relationships among ...

Masimo

Engineer II, AI/Machine Learning

Irvine, CA

$120K - $150K/yr

The AI/Machine Learning Engineer II will be part of the R&D team at Masimo with focus on design and ... The key focus will be in analyzing data from different sources to discover relationships among ...

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Ai Machine Learning Drug Discovery information

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$42.6K

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

As of Jul 20, 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.
Scientist, Machine Learning (Principal Scientist - Associate Director)

Scientist, Machine Learning (Principal Scientist - Associate Director)

Superluminal Medicines, Inc.

Boston, MA • On-site

Other

Re-posted 12 hours ago

Job description

About the Role:

We are seeking a Machine Learning Scientist to join our integrated discovery team and help advance small molecule drug discovery programs through applied ML. In this role, leading from the bench, you will enable the development, validation and deployment of state-of-the-art ML models to generate the quantitative predictions necessary to drive drug discovery. Beyond technical mastery, you will serve as a core strategic partner to medicinal chemists, computational chemists, and biologists, building models that move programs efficiently toward program decision points and candidate nomination.  

Key Responsibilities:

  • Lead the application of Large Language Models (LLMs), co-folding algorithms, and generative chemistry techniques to design novel chemical matter aimed at hitting key program milestones, such as establishing selectivity windows and optimizing drug-like properties
  • Serve as the machine learning POC on cross functional projects partnering  with medicinal chemists and structural biologists to refine SAR and structure informed modeling efforts 
  • Synthesize complex ML outputs into clear, actionable design hypotheses that cross-functional scientific stakeholders can use to make high-stakes program decisions
  • May be responsible for management and development of internal team members

Required Qualifications:

  • Ph.D. in Computational Chemistry, Computer Science, Machine Learning, or a related field
  • 2+ years applying ML methods in a small molecule drug discovery programs in biotech or pharma environments
  • Demonstrated expertise in statistics, probability theory, data modeling, machine learning algorithms, and the languages used to implement analytics solutions
  • Demonstrated success in a cross-functional environment, including biologists, structural biologists, medicinal and computational chemists, with specific examples of computational designs/algorithms/models that directly influence achievement of program milestones
  • Strong practical proficiency in Python and deep learning libraries (e.g., PyTorch, TensorFlow) is required. Demonstrated ability to build and maintain robust, production-quality ML code and data workflows

Preferred Qualifications:

  • Proven experience with protein-ligand co-folding models (e.g.,Boltz, OpenFold, AlphaFold, etc) and the ability to integrate these structural insights into broader ML discovery pipelines
  • Expertise fine-tuning existing models with internally generated structural biology and biology data
  • Strong knowledge of deep learning frameworks, specifically for affinity prediction, ADMET modeling, and the application of LLMs in a biological or chemical context
  • Experience mentoring and developing teams

Skills & Competencies:

  • A demonstrated track record of innovation in the ML/AI space, including developing and validating new architectures or novel applications of existing models to solve complex drug discovery problems
  • Demonstrated expertise using small molecule drug discovery ML/AI tools e.g. AlphaFold, Boltz, OpenFold, ChemProp, DeepChem, Reinvent, etc)  
  • Strong level coding for ML tasks including knowledge of key packages (RDKit, scikit-learn, numpy, pandas, pytorch, DeepChem, polars, PyG/DGL).
  • Strong interpersonal and communications skills in the "why" behind a design to a diverse scientific audience

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