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Machine Learning Engineer Biotech Jobs in Berkeley, CA

Reddit

Machine Learning Engineer

San Francisco, CA · On-site +1

$187K - $260K/yr

Collaborate with other engineers to improve the recommendation systems and models that power personalization and discovery. Train, evaluate, and deploy sophisticated machine learning models to ...

Goodfire

Machine Learning Engineer

San Francisco, CA

$200K - $400K/yr

About the role We're looking for Machine Learning Engineers to help build our platform for training, evaluating, and deploying interpretable AI systems at scale. You'll play a central role in ...

Relace

Machine Learning Engineer

San Francisco, CA · On-site

The Role We're looking for a Machine Learning Engineer who loves getting close to the metal. This is a hands-on engineering role focused on making models faster, more efficient, and more reliable ...

Reddit

Machine Learning Engineer

San Francisco, CA · On-site +1

$187K - $260K/yr

Special Skill Requirements: 1.) Machine Learning; 2.) TensorFlow; 3.) Python and SQL; 4.) Feature Engineering and Selection; 5.) Ads predictive model design; 6.) Ads predictive model offline training ...

Goodfire

Machine Learning Engineer

San Francisco, CA · On-site

$200K - $400K/yr

About the role We're looking for Machine Learning Engineers to help build our platform for training, evaluating, and deploying interpretable AI systems at scale. You'll play a central role in ...

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All JobsMachine Learning Engineer Biotech JobsMachine Learning Engineer Biotech Jobs in Berkeley, CA

Machine Learning Engineer Biotech information

See Berkeley, CA salary details

$38.6K

$157.7K

$236.9K

How much do machine learning engineer biotech jobs pay per year?

As of Jul 5, 2026, the average yearly pay for machine learning engineer biotech in Berkeley, CA is $157,670.00, according to ZipRecruiter salary data. Most workers in this role earn between $124,300.00 and $189,800.00 per year, depending on experience, location, and employer.

What does a Machine Learning Engineer do in the biotech industry?

A Machine Learning Engineer in biotech applies advanced algorithms and data analysis techniques to solve biological and medical problems. They work with large datasets such as genomic sequences, medical images, or clinical records to develop predictive models, automate data analysis, and uncover insights that can accelerate drug discovery, diagnostics, and personalized medicine. Their work often involves close collaboration with biologists, data scientists, and software engineers to create tools and solutions that improve healthcare outcomes. Machine Learning Engineers in this field need a strong background in both computational methods and biological sciences.

How do Machine Learning Engineers in biotech typically collaborate with research scientists and domain experts?

Machine Learning Engineers in biotech often work closely with research scientists and domain experts to translate complex biological problems into data-driven solutions. This collaboration involves regular meetings to understand experimental data, refine project goals, and iterate on model development based on domain feedback. Engineers are expected to communicate technical concepts clearly, adapt models to fit scientific needs, and help validate results alongside laboratory teams. This interdisciplinary environment fosters innovation but also requires flexibility and strong communication skills.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer in Biotech, and why are they important?

To thrive as a Machine Learning Engineer in Biotech, you need a solid background in computer science, statistics, and biology, often with an advanced degree in a related field. Experience with programming languages such as Python or R, machine learning frameworks like TensorFlow or PyTorch, and familiarity with bioinformatics tools are typically required. Strong problem-solving, communication, and interdisciplinary collaboration skills set standout candidates apart. These capabilities are crucial for developing effective models that drive scientific innovation and advance biotechnological research.

What is the difference between Machine Learning Engineer Biotech vs Data Scientist Biotech?

AspectMachine Learning Engineer BiotechData Scientist Biotech
Required CredentialsBachelor's or Master's in Computer Science, Data Science, or related; knowledge of ML frameworksBachelor's or Master's in Data Science, Statistics, or related; strong analytical skills
Work EnvironmentDevelops ML models, coding, deploying algorithms in biotech R&DAnalyzes biological data, interprets results, creates reports
Employer & Industry UsageBiotech firms, pharma companies, research labsBiotech companies, healthcare, research institutions

While both roles work with biological data, Machine Learning Engineers focus on developing and deploying ML algorithms, whereas Data Scientists analyze and interpret biological datasets to inform research and decision-making in biotech settings.

What are popular job titles related to Machine Learning Engineer Biotech jobs in Berkeley, CA? For Machine Learning Engineer Biotech jobs in Berkeley, CA, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer Biotech jobs in Berkeley, CA look for? The top searched job categories for Machine Learning Engineer Biotech jobs in Berkeley, CA are:
What cities near Berkeley, CA are hiring for Machine Learning Engineer Biotech jobs? Cities near Berkeley, CA with the most Machine Learning Engineer Biotech job openings:
Machine Learning Engineer Biotech jobs near you
Infographic showing various Machine Learning Engineer Biotech job openings in Berkeley, CA as of June 2026, with employment types broken down into 63% Full Time, and 37% Contract. Highlights an 100% In-person job distribution, with an average salary of $157,670 per year, or $75.8 per hour.

Senior Machine Learning Engineer

Tahoe Therapeutics

South San Francisco, CA • On-site

$125K - $172K/yr

Full-time

Medical, Dental, Vision, PTO

Posted 5 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 Engineer, 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 - Required
  • Solid Engineering and Computer Science fundamentals, ideally with a degree in CS, Math, or equivalent experience.
  • Exceptional engineering skills to iterate quickly on data processing and training pipelines
  • Experience in building, testing, training, and deploying modern neural network architectures such as Transformers.
  • Experience with frameworks like PyTorch, Tensorflow, Keras, JAX and the ability to write hardware optimized code for distributed training

Qualifications - Desirable
  • Experience with distributed deep learning using frameworks such as HF Accelerate, Deepspeed, Composer, TorchTitan, Megatron etc.
  • Experience with training large neural networks on multiple compute nodes.

Key Responsibilities
  • Stay at the forefront of ML and computational biology research and rapidly adopt state-of-the-art techniques to train AI virtual cell models on our massive single cell datasets.
  • Work together with ML Scientists to develop the next generation of compute optimized models like Tahoe-x1.

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.

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