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Associate Machine Learning Chemistry Jobs in California

Elicit

Machine Learning Engineer

Oakland, CA · On-site +1

... 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 ...

Capgroup

Machine Learning Engineer II

Los Angeles, CA · On-site

$105K - $143K/yr

We believe that belonging leads to better outcomes and a stronger community of associates united by ... In this role you will work with a high performing team of applied scientists, machine learning ...

Capgroup

Machine Learning Engineer II

Irvine, CA · On-site

$104K - $143K/yr

We believe that belonging leads to better outcomes and a stronger community of associates united by ... In this role you will work with a high performing team of applied scientists, machine learning ...

Amgen, Inc.

Sr Machine Learning Scientist

Thousand Oaks, CA · On-site +1

$96K - $131K/yr

Doctorate degree OR Master's degree and 2 years of machine learning experience OR Bachelor's degree and 4 years of machine learning experience OR Associate's degree and 8 years of machine learning ...

Capgroup

Senior Machine Learning Engineer

Irvine, CA

$112K - $154K/yr

We believe that belonging leads to better outcomes and a stronger community of associates united by ... Design and implement scalable, production-quality systems that incorporate machine learning and ...

Amgen

Sr Machine Learning Scientist

Thousand Oaks, CA · On-site

$96K - $131K/yr

Machine Learning Scientist What you will do Let'sdo this.Let'schange the world.Within Amgen ... and 4 years ofmachine learningexperience OR Associate's degree and 8 years ...

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All JobsAssociate Machine Learning Chemistry JobsAssociate Machine Learning Chemistry Jobs in California

Associate Machine Learning Chemistry information

What is the difference between Associate Machine Learning Chemistry vs Associate Data Scientist?

AspectAssociate Machine Learning ChemistryAssociate Data Scientist
Required CredentialsBachelor's or Master's in Chemistry, Data Science, or related fields; familiarity with ML frameworksBachelor's or Master's in Data Science, Statistics, Computer Science; programming skills in Python/R
Work EnvironmentResearch labs, pharmaceutical or chemical companies, biotech firmsTech companies, finance, healthcare, consulting firms
Employer & Industry UsageUsed in industries applying ML to chemical data, drug discovery, materials scienceApplied across industries analyzing large datasets, predictive modeling

Associate Machine Learning Chemistry focuses on applying machine learning techniques specifically to chemical and scientific data, often within research or pharmaceutical settings. In contrast, Associate Data Scientist has a broader scope, working with various data types across multiple industries. Both roles require strong analytical skills and familiarity with ML tools, but their industry focus and data types differ.

What are Associate Machine Learning Chemists?

Associate Machine Learning Chemists are professionals who combine expertise in chemistry with skills in machine learning to analyze chemical data, develop predictive models, and accelerate scientific discovery. They often work on tasks like predicting molecular properties, optimizing chemical reactions, and supporting drug discovery efforts using computational tools. Typically, these roles require a strong foundation in chemistry, programming experience (often in Python), and familiarity with machine learning libraries. Associate positions are generally entry-level or early-career roles, providing support to senior scientists and data scientists in research and development teams.

How does an Associate Machine Learning Chemistry professional typically collaborate with research scientists and engineers?

As an Associate Machine Learning Chemistry professional, you will frequently work alongside research scientists and chemical engineers to develop predictive models and analyze experimental data. Collaboration involves translating chemical problems into machine learning tasks, sharing insights from model results, and participating in interdisciplinary meetings to refine research objectives. Effective communication and teamwork are essential, as you may be required to explain machine learning concepts to non-technical colleagues and integrate their domain expertise into your models. This collaborative environment fosters both scientific discovery and professional growth.

What are the key skills and qualifications needed to thrive as an Associate Machine Learning Chemistry, and why are they important?

To thrive as an Associate Machine Learning Chemistry professional, you need a solid background in chemistry, data analysis, and machine learning, typically supported by a relevant degree such as chemistry, computer science, or a related field. Experience with programming languages like Python, machine learning libraries (e.g., TensorFlow, scikit-learn), and cheminformatics software is highly valued. Strong problem-solving skills, attention to detail, and the ability to communicate complex concepts clearly are crucial soft skills. These competencies enable effective collaboration on interdisciplinary teams and the development of innovative solutions in computational chemistry research.
What are the most commonly searched types of Machine Learning Chemistry jobs in California? The most popular types of Machine Learning Chemistry jobs in California are:
What are popular job titles related to Associate Machine Learning Chemistry jobs in California? For Associate Machine Learning Chemistry jobs in California, the most frequently searched job titles are:
What job categories do people searching Associate Machine Learning Chemistry jobs in California look for? The top searched job categories for Associate Machine Learning Chemistry jobs in California are:
What cities in California are hiring for Associate Machine Learning Chemistry jobs? Cities in California with the most Associate Machine Learning Chemistry job openings:
Associate Machine Learning Chemistry jobs near you

Senior Machine Learning Scientist

Tahoe Therapeutics

South San Francisco, CA

$200K - $275K/yr

Full-time

Re-posted 3 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.
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.