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Ai Machine Learning Drug Discovery information

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

As of Sep 8, 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 August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 24% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.

Machine Learning Engineer - Drug Discovery

Astrix Inc

South San Francisco, CA • On-site

$60 - $70/hr

Full-time, Contractor

Posted 24 days ago

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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