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Machine Learning Computational Biology Jobs in California

Onebridge

Computational Biology MLOps Engineer

San Diego, CA · On-site

$118K - $139K/yr

You bridge cutting edge AI research and production systems at the intersection of machine learning, computational biology, and high performance computing. You thrive in cross functional environments ...

Onebridge

Computational Biology MLOps Engineer

San Diego, CA · On-site +1

$118K - $139K/yr

You bridge cutting edge AI research and production systems at the intersection of machine learning, computational biology, and high performance computing. You thrive in cross functional environments ...

Verge Genomics

Computational Biologist

San Francisco, CA · On-site +1

... machine learning objectives. Requirements Candidates must have : * Either: * PhD in computational biology, AI/ML, applied statistics, biophysics, or , * MS and professional experience in relevant ...

LLNL

Machine Learning (ML) Bioengineer

Livermore, CA · On-site

$9.7K/wk

You will join the Bioresilience Incubator, a dynamic engineering center that integrates predictive computational modeling, machine learning, and experimental biology to advance national security and ...

LLNL

Machine Learning (ML) Bioengineer

Livermore, CA · On-site

You will join the Bioresilience Incubator, a dynamic engineering center that integrates predictive computational modeling, machine learning, and experimental biology to advance national security and ...

LLNL

Machine Learning (ML) Bioengineer

Livermore, CA · On-site

You will join the Bioresilience Incubator, a dynamic engineering center that integrates predictive computational modeling, machine learning, and experimental biology to advance national security and ...

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All JobsMachine Learning Computational Biology JobsMachine Learning Computational Biology Jobs in California

Machine Learning Computational Biology information

What types of projects might I work on as a Machine Learning Computational Biology specialist?

As a Machine Learning Computational Biology specialist, you may work on projects ranging from analyzing large-scale genomic data to developing predictive models for disease risk or drug response. Typical tasks include designing and implementing machine learning algorithms to identify patterns in biological datasets, collaborating with biologists and clinicians to interpret results, and contributing to publications or presentations. You'll often be part of a multidisciplinary team, interacting with data scientists, laboratory researchers, and software engineers. This role offers the opportunity to work on cutting-edge biomedical research and have a direct impact on advancements in healthcare and life sciences.

What is a Machine Learning Computational Biology job?

A Machine Learning Computational Biology job involves applying machine learning techniques to analyze biological data, such as genomics, proteomics, and medical imaging. Professionals in this field develop algorithms and models to identify patterns, make predictions, and generate insights that can drive scientific discovery or improve healthcare. They typically work with large datasets, employing statistical and computational methods to solve complex biological problems. The role often requires expertise in programming, data science, and domain-specific biological knowledge. It is commonly found in academia, pharmaceutical companies, biotech firms, and healthcare institutions.

What are the key skills and qualifications needed to thrive in the Machine Learning Computational Biology position, and why are they important?

To thrive as a Machine Learning Computational Biology professional, you need a strong background in biology, statistics, computer science, and machine learning, typically supported by an advanced degree in a relevant field. Familiarity with programming languages such as Python or R, experience with bioinformatics tools, and knowledge of machine learning frameworks like TensorFlow or scikit-learn are commonly required. Strong analytical thinking, effective communication, and the ability to work collaboratively in interdisciplinary teams are highly valued soft skills. These qualifications are essential for solving complex biological problems, developing robust computational models, and effectively communicating findings to both technical and non-technical stakeholders.

What are popular job titles related to Machine Learning Computational Biology jobs in California? For Machine Learning Computational Biology jobs in California, the most frequently searched job titles are:
What job categories do people searching Machine Learning Computational Biology jobs in California look for? The top searched job categories for Machine Learning Computational Biology jobs in California are:
What cities in California are hiring for Machine Learning Computational Biology jobs? Cities in California with the most Machine Learning Computational Biology job openings:
Machine Learning Computational Biology jobs near you
Infographic showing various Machine Learning Computational Biology job openings in California as of July 2026, with employment types broken down into 71% Full Time, 25% Part Time, 1% Temporary, and 3% Contract. Highlights an 78% Physical, 2% Hybrid, and 20% Remote job distribution.
Computational Biologist MLOps Engineer

Computational Biologist MLOps Engineer

Marlabs

San Diego, CA • On-site

Other

Posted 2 days ago

Job description

Marlabs, a global AI and Digital Solutions Consulting firm, delivers intelligent solutions across AI, data, analytics, and product engineering. Since 2000, we have partnered with some of the largest healthcare, life sciences, financial services, and government organizations worldwide. As we continue to expand our global footprint, we have an exciting opportunity for a highly skilled Computational Biology MLOps Engineer to join our innovative and dynamic team.


This role requires onsite work three (3) days per week in Indianapolis, IN or San Diego, CA.


Computational Biology MLOps Engineer | About You

As a Computational Biology MLOps Engineer, you are responsible for building and scaling the ML infrastructure that supports next generation in silico protein design and engineering. You bridge cutting edge AI research and production systems at the intersection of machine learning, computational biology, and high performance computing. You thrive in cross functional environments and partner closely with computational scientists and platform engineers to accelerate research velocity. You bring strong software, DevOps, and data engineering fundamentals with hands on experience across CI/CD, orchestration, and distributed training. Experience working with scientific or multimodal data and interest in protein language and generative models is a plus.


Computational Biology MLOps Engineer | Day-to-Day

  • Build and maintain ML infrastructure, including CI/CD pipelines (GitHub Actions) for model training, evaluation, and deployment.
  • Orchestrate compute across Kubernetes clusters and SLURM and HPC environments to optimize utilization for large scale training.
  • Develop robust and scalable data pipelines that deliver ML ready datasets from biological sources such as PDB and mmCIF files, sequence databases, and assay readouts.
  • Create tools and frameworks that enable rapid iteration on protein language models, diffusion models, and other generative approaches.
  • Architect systems that scale across distributed environments and support multimodal datasets for large foundational models.
  • Implement monitoring, logging, and alerting to ensure reliability, performance, and cost efficiency of production ML systems.


Computational Biology MLOps Engineer | Skills & Experience

  • 5+ years of overall industry experience in software engineering, DevOps, data engineering, or ML engineering roles, including 3+ years of focused MLOps experience building and maintaining production grade ML infrastructure.
  • Proven CI/CD expertise with GitHub Actions and strong DevOps practices including infrastructure as code, version control, and collaborative workflows.
  • Hands on Kubernetes experience in deploying and managing containerized ML workloads with familiarity using container registries.
  • Proficiency with SLURM or similar job schedulers in HPC environments and experience with distributed training optimization including mixed precision and checkpointing.
  • Strong Python skills and experience with major ML frameworks including PyTorch, TensorFlow, or JAX.
  • Experience building ETL processes and scalable data and feature pipelines and experience with cloud platforms such as AWS, GCP, or Azure.
  • Preferred experience with scientific data, protein structure formats such as PDB and mmCIF, protein AI models including ESM, and agentic systems such as MCP, LangGraph, and LangChain.

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