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

As of Jul 14, 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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What cities are hiring for Ai Machine Learning Drug Discovery jobs? Cities with the most Ai Machine Learning Drug Discovery job openings:
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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.
Senior/Principal Machine Learning Scientist, Structure and Simulation (AI for Drug Discovery)

Senior/Principal Machine Learning Scientist, Structure and Simulation (AI for Drug Discovery)

Genentech

Manhattan, NY • On-site

Full-time

Re-posted 25 days ago

Genentech rating

8.8

Company rating: 8.8 out of 10

Based on 22 frontline employees who took The Breakroom Quiz

10th of 74 rated pharmaceutical

Job description

Job Summary:
Genentech is a company dedicated to innovating healthcare for a healthier future. They are seeking experienced machine learning scientists to develop novel methods for drug discovery, focusing on integrating diverse data sources and consolidating machine learning projects into a coherent model for therapeutic discovery.
Responsibilities:
• Develop novel machine learning methods to answer challenging research questions in large molecule drug discovery
• Work with biological and chemical data from heterogeneous sources
• Contribute to an initiative to consolidate projects in machine learning theory into a single coherent model for lab-in-the-loop drug discovery
• Lead an initiative to consolidate projects in machine learning theory into a single coherent model for lab-in-the-loop drug discovery
Qualifications:
Required:
• Significant education in computer science or the life and physical sciences, or equivalent work experience: for example, anything from a BS+7 to PhD+2 years, with experience designing and building machine learning systems, particularly for molecules and biological sequences.
• Demonstrated experience with Python and deep learning libraries such as PyTorch, TensorFlow, or JAX.
• Familiarity with areas of modern machine learning research, such as reinforcement learning, sampling, and multimodal representation learning.
• Demonstrated research experience, including at least one first author publication or equivalent.
• Strong communication and collaboration skills
• Portfolio of computational projects (available on e.g. GitHub)
Company:
Genentech is a biotechnology research company that specializes in genetic testing and personalized medicines. Founded in 1976, the company is headquartered in South San Francisco, USA, with a team of 10001+ employees. The company is currently Late Stage.

What Genentech employees say

Pay

Benefits

Hours and flexibility

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About Genentech

Sourced by ZipRecruiter

A member of the Roche Group, Genentech has been at the forefront of the biotechnology industry for more than 40 years, using human genetic information to develop novel medicines for serious and life-threatening diseases. Genentech has multiple therapies on the market for cancer & other serious illnesses. Please take this opportunity to learn about Genentech where we believe that our employees are our most important asset & are dedicated to remaining a great place to work.

Industry

Scientific research and development services

Company size

10,000+ Employees

Headquarters location

South San Francisco, CA, US

Year founded

1976

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