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Machine Learning Chemistry Jobs in Berkeley, CA (NOW HIRING)

Elicit

Machine Learning Engineer

Oakland, CA · On-site +1

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

... role As a Machine Learning Engineer at Elicit, you'll build products and workflows that help ... Build a target-assessment workflow that combines literature, genetics, chemistry, clinical ...

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Showing results 21-40

All JobsMachine Learning Chemistry JobsMachine Learning Chemistry Jobs in Berkeley, CA

Machine Learning Chemistry information

See Berkeley, CA salary details

$16

$27

$39

How much do machine learning chemistry jobs pay per hour?

As of Aug 19, 2026, the average hourly pay for machine learning chemistry in Berkeley, CA is $27.25, according to ZipRecruiter salary data. Most workers in this role earn between $22.36 and $30.00 per hour, depending on experience, location, and employer.

What is a machine learning chemistry?

A Machine Learning Chemistry job involves using artificial intelligence techniques to analyze chemical data, model molecular behaviors, and accelerate discoveries in chemistry-related fields. Professionals in this role develop and apply machine learning algorithms to predict chemical properties, optimize reactions, and assist in drug design, material science, and other applications. They typically work in pharmaceuticals, materials science, or environmental chemistry, collaborating with chemists, data scientists, and engineers to solve complex chemical problems efficiently.

What does a machine learning chemistry do?

Professionals in Machine Learning Chemistry often work on projects such as developing predictive models for chemical property analysis, optimizing molecular structures, or advancing drug discovery through data-driven methods. Daily tasks may include data preprocessing, building and training machine learning models, validating results, and interpreting outcomes in collaboration with experimental chemists. Teamwork is common, with regular interactions between chemistry researchers, data scientists, and software engineers. This structure allows for iterative feedback and ensures that computational models align with practical lab needs. Continuous learning and adaptation are also key, as both the chemistry and machine learning fields are rapidly evolving.

What are the key skills and qualifications needed to thrive in machine learning chemistry?

To thrive in a Machine Learning Chemistry role, you need a solid background in chemistry, expertise in data science and machine learning algorithms, and typically an advanced degree in chemistry, computer science, or a related field. Familiarity with programming languages like Python or R and experience working with cheminformatics tools and machine learning frameworks (such as TensorFlow or scikit-learn) are essential. Strong analytical thinking, problem-solving abilities, and effective communication skills enable professionals to bridge the gap between computational work and experimental research teams. These competencies are crucial for developing innovative solutions in chemical research and ensuring successful collaboration across interdisciplinary teams.

What are popular job titles related to Machine Learning Chemistry jobs in Berkeley, CA?

For Machine Learning Chemistry jobs in Berkeley, CA, the most frequently searched job titles are:

What job categories do people searching Machine Learning Chemistry jobs in Berkeley, CA look for?

The top searched job categories for Machine Learning Chemistry jobs in Berkeley, CA are:

What cities near Berkeley, CA are hiring for Machine Learning Chemistry jobs?

Cities near Berkeley, CA with the most Machine Learning Chemistry job openings:

Machine Learning Chemistry jobs near you
Infographic showing various Machine Learning Chemistry job openings in Berkeley, CA as of August 2026, with employment types broken down into 4% Internship, 74% Full Time, 18% Part Time, and 4% Nights. Highlights an 100% In-person job distribution, with an average salary of $56,682 per year, or $27.3 per hour.

Senior Machine Learning Scientist

Tahoe Therapeutics

South San Francisco, CA

$200K - $275K/yr

Full-time

Medical, Dental, Vision, PTO

Re-posted 6 days ago

Job description

About Tahoe Therapeutics
Tahoe Therapeutics is a biotechnology company pioneering a fundamentally new approach to drug discovery, one that begins with the biology of real patients. Our Mosaic platform is the first to make in vivo data generation scalable, with single-cell resolution, allowing us to map how drugs affect patient-derived cells in the body across a wide range of biological contexts. We are building the world's largest in vivo single-cell perturbation atlas and using it to train multimodal foundation models that learn the context-dependent nature of gene function, disease progression, and drug response.By combining cutting-edge machine learning with the most biologically relevant datasets ever assembled in drug discovery, our mission is to find better drugs, faster and bring them to more patients who need them.

Your role
With Tahoe-100M, we solved one of the fundamental bottlenecks in building a virtual model of the cell: generating massive, perturbation-rich, single-cell datasets that capture real biological causality. With Tahoe-x1, we removed the second bottleneck: creating a modern platform for rapid iteration on model architectures and designs in a cost-efficient manner and at scale. At Tahoe, we embody a simple philosophy: build in the open, shoot for the moon, and we're looking for people who want to push the frontier of what's possible.

As a Senior Machine Learning Scientist, you will play a leading role in designing the next generation of foundation models of gene regulatory networks powered by Tahoe's large scale single-cell datasets such as Tahoe-100M and beyond. This role is well-suited for someone with a strong background in machine learning and statistics, and an interest in applying cutting-edge breakthroughs in ML to meaningful problems in drug discovery. We are looking for non-incremental thinkers with the skills to help build models that can make a real impact on drug discovery.
Qualifications - Essential
  • PhD or equivalent practical experience in a technical field.
  • A proven track record of developing and applying deep learning methods, including experience with modern architectures such as transformers, state-space models, graph neural networks or diffusion-based generative models.
  • Proficiency with modern ML frameworks (e.g., PyTorch, JAX, or TensorFlow) and core scientific computing libraries (e.g., NumPy, SciPy, Pandas).
  • A genuine enthusiasm for applying cutting-edge ML research to real-world biological problems and a bias towards action.
Qualifications - Nice to have
  • Prior experience with ML applied to problems in biology or chemistry.
  • Familiarity with multimodal modeling, contrastive learning or self-supervised learning.
  • Experience with large scale distributed ML techniques (e.g., FSDP, TP, dMoE, flash attention)
Key Responsibilities
  • Develop and apply machine learning techniques towards building multimodal foundation models that bridge the chemical and biological domains, i.e.: integrate models of chemical structure, target protein sequence and whole transcriptome scRNAseq.
  • Stay at the forefront of ML and computational biology research and rapidly adopt state-of-the-art techniques to our problems and datasets.
  • Collaborate with our team of biologists and engineers in cross-functional pods to test novel ML-driven hypotheses.
Benefits
  • Unlimited Paid Time Off (PTO).
  • Monthly Lunch budget.
  • One-time Office set up budget.
  • US Employees: HMO Kaiser Platinum and PPO Anthem Gold medical as well as vision and dental plans for both the employee and dependents.
$200,000 - $275,000 a year
This position requires on-site presence at our South San Francisco office a minimum of three days per week.

We welcome applicants who require visa sponsorship and provide work authorization support for qualified candidates.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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