1

Machine Learning Computational Chemistry Jobs in San Ramon, CA

No prior AI or machine learning experience is required. Training will be provided on project ... Computational Chemistry. • Strong familiarity with modern laboratory methods and computational ...

PhD in computer science, machine learning, computational neuroscience, or related fields (or equivalent industry experience). * Expertise in deep learning frameworks (e.g., PyTorch, TensorFlow) and ...

PhD in computer science, machine learning, computational neuroscience, or related fields (or equivalent industry experience). * Expertise in deep learning frameworks (e.g., PyTorch, TensorFlow) and ...

... Computational Chemistry, Data Engineering, Data Modeling, Data Science, Data Visualization, Environmental Toxicology, Foundation Engineering, Large Language Models (LLMs), Machine Learning (ML ...

Showing results 41-60

Machine Learning Computational Chemistry information

What is machine learning computational chemistry?

Machine learning computational chemistry is a field that combines machine learning techniques with computational chemistry to accelerate the discovery and design of molecules and materials. By training algorithms on large datasets of chemical information, researchers can predict molecular properties, simulate chemical reactions, and optimize compounds more efficiently than traditional methods. This approach helps reduce the time and cost required for research in drug discovery, materials science, and related fields.

What are some common challenges faced by professionals working in machine learning computational chemistry roles?

One common challenge in Machine Learning Computational Chemistry roles is integrating large and often complex chemical datasets with appropriate machine learning models, which requires a solid understanding of both domains. Professionals may also encounter difficulties in ensuring that their models are both interpretable and generalizable to new data, as overfitting is a frequent issue. Additionally, collaboration with chemists and data scientists is essential, so clear communication across disciplines is key to success. Staying up to date with the latest developments in both computational chemistry and machine learning is crucial for ongoing professional growth.

What are the key skills and qualifications needed to thrive as a machine learning computational chemist, and why are they important?

To thrive as a Machine Learning Computational Chemist, you need a solid background in chemistry, mathematics, and computer science, typically supported by an advanced degree in computational chemistry, cheminformatics, or a related field. Proficiency with programming languages (such as Python), machine learning frameworks (like TensorFlow or PyTorch), and molecular modeling software is essential. Strong analytical thinking, problem-solving skills, and effective collaboration are key soft skills that help drive innovation and teamwork. These skills and qualifications are critical for developing accurate models, advancing research, and translating computational insights into real-world chemical solutions.

What is the difference between Machine Learning Computational Chemistry vs Computational Chemist?

AspectMachine Learning Computational ChemistryComputational Chemist
Required CredentialsAdvanced degrees in chemistry, computer science, or related fields; knowledge of machine learning and programmingDegree in chemistry, chemical engineering, or related fields; strong background in chemical theory and modeling
Work EnvironmentResearch labs, tech companies, academia; focus on algorithm development and data analysisLaboratories, research institutions, industry; focus on chemical modeling and simulation
Employer & Industry UsageTech firms, pharmaceutical companies, research institutions applying AI/ML techniquesPharmaceutical, chemical, and materials industries conducting chemical research and development

Machine Learning Computational Chemists specialize in applying machine learning algorithms to chemical data, enhancing predictive models and simulations. Computational Chemists focus on traditional chemical modeling and simulations using computational methods. Both roles require strong chemistry backgrounds, but Machine Learning Computational Chemists emphasize data science and AI skills, while Computational Chemists focus on chemical theory and modeling techniques.

What cities near San Ramon, CA are hiring for Machine Learning Computational Chemistry jobs?

Cities near San Ramon, CA with the most Machine Learning Computational Chemistry job openings:

Infographic showing various Machine Learning Computational Chemistry job openings in San Ramon, CA as of August 2026, with employment types broken down into 1% As Needed, 71% Full Time, 25% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

ML Engineer, Biological Analysis & Simulation

Mithrl

San Francisco, CA • On-site

$150K - $200K/yr

Full-time

Medical, Dental, Vision, Retirement

Re-posted 25 days ago


Job description

ABOUT MITHRL

We imagine a world where new medicines reach patients in months, not years, and where scientific breakthroughs happen at the speed of thought.

Mithrl is building the world’s first commercially available AI Co-Scientist. It is a discovery engine that transforms messy biological data into insights in minutes. Scientists ask questions in natural language, and Mithrl responds with real analysis, novel targets, hypotheses, and patent-ready reports.

Our traction speaks for itself:

  • 12X year-over-year revenue growth

  • Trusted by leading biotechs and big pharma across three continents

  • Driving real breakthroughs from target discovery to patient outcomes.

ABOUT THE ROLE

We are hiring an ML Engineer, Analysis and Simulation to build the core analytical and reasoning layer behind the Mithrl AI Co-Scientist. Your work will define how the AI interprets biological datasets, generates scientific conclusions, and orchestrates downstream simulation tools for drug discovery.

You will develop the reusable analysis modules that Mithrl runs for every dataset, and you will design multi step agentic workflows that combine statistical analysis, biological reasoning, and computational modeling. You will also integrate and experiment with simulation tools for small molecule discovery, such as ADMET prediction, docking scoring, Boltzmann generators and related computational chemistry engines.

This is the role that makes the AI Co-Scientist smart. If you have a strong background in ML, computational biology, and scientific analysis workflows, and you want to shape how AI reasons about biological systems, this is an exceptional opportunity.

WHAT YOU WILL DO

  • Build AI driven analysis agents that perform biological reasoning across a wide range of datasets

  • Develop the standard analysis suite for each dataset, including modules for differential expression, pathway analysis, feature importance, clustering, scoring, enrichment, and mechanism-of-action interpretation

  • Build multi step workflows that combine ML models, statistical logic, and biological knowledge to produce high confidence insights

  • Design and implement agentic reasoning strategies that allow Mithrl to run dozens analyses per dataset and synthesize the outputs into a coherent scientific narrative

  • Integrate simulation and modeling tools for small molecule drug discovery, including ADMET prediction, docking scoring, generative chemistry tools, structure based modeling, and related computational frameworks

  • Collaborate with the data engineering, bioinformatics, and curation teams to ensure analysis modules operate on clean and consistent data

  • Validate results, benchmark pipelines, and ensure scientific accuracy and reproducibility of all analyses

  • Contribute to the long term architecture for how the AI Co-Scientist performs reasoning, hypothesis testing, and simulation

WHAT YOU BRING

Required Qualifications

  • Strong experience in machine learning, computational biology, or a related scientific ML field

  • Experience developing analysis modules for biological or scientific datasets

  • Familiarity with common techniques in target discovery, gene expression analysis, pathway inference, clustering, or statistical modeling

  • Hands-on experience with computational chemistry or simulation tools, such as ADMET models, docking, binding prediction, or molecular generative models

  • Proficiency in Python and scientific computing libraries

  • Experience designing multi step reasoning or workflow based ML pipelines

  • Ability to translate messy scientific questions into structured ML or analytical workflows

  • Strong communication skills and comfort collaborating with cross functional scientific and engineering teams

Nice to Have

  • Experience with LLM powered scientific agents or multi agent architectures

  • Familiarity with phenotype based discovery, multi modal integration, or systems biology

  • Background in computational chemistry or structure based drug discovery

  • Experience with biological ontologies, curated knowledge graphs, or pathway databases

  • Prior experience in a tech bio company, biotech R&D group, or scientific platform team

WHAT YOU WILL LOVE AT MITHRL

  • High ownership: You will define how the AI Co-Scientist thinks and reasons about biology

  • Impact: You will work at the intersection of ML, biology, and simulation, with direct impact on real discovery programs

  • Team: Join a tight-knit, talent-dense team of engineers, scientists, and builders

  • Culture: We value consistency, clarity, and hard work. We solve hard problems through focused daily execution

  • Speed: We ship fast (2x/week) and improve continuously based on real user feedback

  • Location: Beautiful SF office with a high-energy, in-person culture

  • Benefits: Comprehensive PPO health coverage through Anthem (medical, dental, and vision) + 401(k) with top-tier plans

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.

Compensation Range: $150K - $200K