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Senior Machine Learning Chemistry Jobs in California

Leidos

Senior Machine Learning Engineer

Vista, CA · On-site

$107K - $195K/yr

We are seeking a Senior Machine Learning Engineer to work on MLOPS that support the testing, and release of object detection algorithms for our portfolio of products that help safeguard the flow of ...

Leidos

Senior Machine Learning Engineer

Vista, CA · On-site

$107K - $195K/yr

We are seeking a Senior Machine Learning Engineer to work on MLOPS that support the testing, and release of object detection algorithms for our portfolio of products that help safeguard the flow of ...

TEKsystems

Sr. Machine Learning Engineer

Menlo Park, CA · Hybrid

$70 - $85/hr

Senior Machine Learning Engineer (LLM Evaluation & AI Agents) Location: Menlo Park, CA (Hybrid) Employment Type: 6-Month Contract Compensation: $70.00-$85.00/hour (W-2) Help Advance the Next ...

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Senior Machine Learning Chemistry information

Is senior machine learning chemistry used in chemistry?

Senior machine learning chemists apply advanced algorithms to analyze chemical data, predict molecular properties, and accelerate research in chemistry. Their work often involves using tools like neural networks and data modeling to improve understanding and innovation in chemical sciences.

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

AspectSenior Machine Learning ChemistryData Scientist
Required CredentialsAdvanced degrees in Chemistry, Data Science, or related fields; experience with machine learning and chemistry-specific toolsDegrees in Computer Science, Statistics, or related fields; proficiency in programming, statistics, and data analysis
Work EnvironmentResearch labs, pharmaceutical companies, biotech firms focusing on chemical data analysisTech companies, finance, healthcare, and various industries analyzing large datasets
Employer & Industry UsageUsed in industries combining chemistry and machine learning, such as drug discovery and materials scienceWidely used across industries for data analysis, predictive modeling, and business insights

While both roles involve data analysis and machine learning, Senior Machine Learning Chemistry specializes in applying these skills to chemical data and research, whereas Data Scientists work across diverse industries analyzing various types of data. The choice depends on your industry focus and expertise in chemistry versus general data analysis.

What are the key skills and qualifications needed to thrive as a senior machine learning chemist?

To thrive as a Senior Machine Learning Chemist, you need a strong background in chemistry, expertise in machine learning algorithms, and an advanced degree (typically a PhD) in chemistry, computational science, or a related field. Proficiency with programming languages like Python, cheminformatics tools (such as RDKit), and experience with data analysis platforms and machine learning frameworks (e.g., TensorFlow, PyTorch) are crucial. Strong problem-solving skills, effective communication, and the ability to work collaboratively across interdisciplinary teams set successful candidates apart. These skills and qualities are vital for developing innovative solutions, translating chemical data into actionable insights, and advancing research and development in the field.

How does a senior machine learning chemistry professional typically collaborate with cross-functional teams in research and development projects?

Senior Machine Learning Chemistry professionals frequently collaborate with interdisciplinary teams, including computational chemists, data scientists, and experimental researchers. They play a key role in translating complex chemical problems into machine learning models, guiding data collection, and interpreting results to inform experimental design. Regular communication and joint planning sessions are common, ensuring that machine learning solutions align with project goals and that model outputs are actionable for the broader R&D team. This collaborative environment fosters innovation and accelerates the development of new materials or drugs.

What does a senior machine learning chemist do?

A Senior Machine Learning Chemist applies advanced machine learning techniques to solve complex problems in chemistry, such as predicting molecular properties, designing new materials, or accelerating drug discovery. They often work at the intersection of chemistry, computer science, and data analysis, collaborating with interdisciplinary teams. Their responsibilities include developing and optimizing algorithms, analyzing chemical data, and translating scientific questions into computational models to drive innovation in the chemical and pharmaceutical industries.
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 Senior Machine Learning Chemistry jobs in California? For Senior Machine Learning Chemistry jobs in California, the most frequently searched job titles are:
What job categories do people searching Senior Machine Learning Chemistry jobs in California look for? The top searched job categories for Senior Machine Learning Chemistry jobs in California are:
What cities in California are hiring for Senior Machine Learning Chemistry jobs? Cities in California with the most Senior Machine Learning Chemistry job openings:
Senior Machine Learning Chemistry jobs near you

Senior Machine Learning Scientist I, Drug Discovery Analytics

Revolution Medicines

Redwood City, CA • Hybrid

$112K - $153K/yr

Full-time

Re-posted 14 days ago

Job description

The Opportunity:

We are seeking a Senior Machine Learning Scientist to help accelerate drug discovery through advanced analytics and artificial intelligence. This role will develop predictive models and analytical methods that transform complex biological and chemical datasets into actionable insights that guide research decisions.

The Senior Machine Learning Scientist will work at the interface of data science, chemistry, and biology to support target discovery, compound optimization, and translational research. This position requires both strong machine learning expertise and the ability to collaborate effectively with experimental scientists to solve real-world scientific problems.

The successful candidate will contribute to building a data-driven discovery ecosystem where data, analytics, and experimentation continuously inform and accelerate one another.
Key responsibilities include:

  • Develop Predictive Models for Drug Discovery.

  • Independently Design and implement machine learning models to predict compound activity, selectivity, and developability.

  • Identify and Develop predictive frameworks for ADME/Tox, target engagement, and phenotypic screening outcomes.

  • Apply advanced modeling approaches including deep learning, graph neural networks, and ensemble methods.

  • Evaluate model performance and apply appropriate validation strategies.

  • Work with data engineers and ML engineers to integrate models into discovery pipelines.

  • Analyze Complex Scientific Data.

  • Perform exploratory data analysis on chemical, biological, and phenotypic datasets.

  • Integrate heterogeneous datasets including:

  • Chemical structure and screening data.

  • Structural biology and molecular simulation outputs.

  • Collaborate with Research Scientists.

  • Partner with medicinal chemists to support compound design and lead optimization.

  • Work with biologists to interpret experimental results and identify new target opportunities.

  • Translate scientific questions into computational modeling strategies.

Required Skills, Experience and Education:

  • PhD in machine learning, computational biology, computational chemistry, computer science, statistics, or a related quantitative field.

  • 6-10 years of experience applying machine learning or advanced analytics to scientific datasets.

  • Python and scientific computing libraries (NumPy, Pandas, SciPy).

  • Machine learning frameworks (PyTorch, TensorFlow, scikit-learn).

  • Model development, validation, and evaluation methods.

  • Data visualization and exploratory analysis.

  • Experience working with noisy and incomplete experimental datasets.

Preferred Skills:

  • Cheminformatics or molecular modeling tools (RDKit, OpenEye, etc.).

  • Multi-omics data analysis.

  • Cloud computing environments.

  • MLOps or scalable model deployment. 

    #LI-Hybrid  #LI-LN1

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