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Machine Learning Engineer Biotech Jobs in Fairfield, CA

Hive

Senior Machine Learning Engineer

San Francisco, CA

$160K - $250K/yr

Senior Machine Learning Engineer In order to execute our vision, we need to grow our team of best-in-class machine learning engineers. We are looking for developers who are excited about staying at ...

Warnerbros

Senior Machine Learning Engineer

San Francisco, CA · On-site

$123K - $169K/yr

Senior Machine Learning Engineer Team: Data & Audience Platform (DAP) - ML Engineering What We Do Warner Bros. Discovery (WBD) is home to the world's most iconic entertainment, news, and sports ...

Onyx

Machine Learning Engineer

San Francisco, CA · On-site

$150K - $250K/yr

Have 3+ years of AI/ML engineering experience building real-world applications * have in-depth experience with PyTorch/Tensorflow, NLP models, and standard ML algorithms * Are up date with new ...

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

Machine Learning Engineer Biotech information

See Fairfield, CA salary details

$32.1K

$131K

$196.9K

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

As of Aug 6, 2026, the average yearly pay for machine learning engineer biotech in Fairfield, CA is $131,047.00, according to ZipRecruiter salary data. Most workers in this role earn between $103,300.00 and $157,700.00 per year, depending on experience, location, and employer.

What does a machine learning engineer do in biotech?

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?

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 Fairfield, CA? For Machine Learning Engineer Biotech jobs in Fairfield, CA, the most frequently searched job titles are:
What cities near Fairfield, CA are hiring for Machine Learning Engineer Biotech jobs? Cities near Fairfield, CA with the most Machine Learning Engineer Biotech job openings:
Machine Learning Engineer Biotech jobs near you

Machine Learning Engineer - Quality Intelligence

AfterQuery

San Francisco, CA • On-site

$200K - $300K/yr

Full-time

Posted 25 days ago

Job description

About AfterQuery
AfterQuery is an applied research lab curating data solutions for foundation model development.
We serve every frontier AI lab with the mission of delivering the best data to power the best models. In doing so, we can make expertise that once took a lifetime to build available to anyone who needs it. Our customers are the ones building the foundation models themselves and our work sits directly in the loop of how those systems improve.
This is a rare opportunity to join a company at a defining moment in AI. Since raising our $30M Series A at a $300M valuation, AfterQuery has grown well over a $100M revenue run rate.
We're based in San Francisco and backed by leading investors including Altos Ventures, BoxGroup, and Y Combinator and angels from Google DeepMind, OpenAI, Anthropic, Meta Superintelligence Labs, and Microsoft AI.
Machine Learning Engineer, Quality Intelligence
Overview
AfterQuery builds the data and evaluation systems that power frontier AI models. Every leading AI lab uses our datasets and reinforcement learning environments to encode and scale real-world expertise.
We're hiring a Founding Machine Learning Engineer, Quality Intelligence to build the ML systems behind how we measure, improve, and scale data quality. You'll work on production systems at the intersection of machine learning, human expertise, and frontier model evaluation.
This role is for someone who wants to build practical ML systems that directly improve the quality, reliability, and scalability of expert human data.
Responsibilities
  • Build ML and data systems that help measure quality across complex human data workflows
  • Develop systems for expert matching, quality prediction, and anomaly detection
  • Build evaluation infrastructure for tasks, reviewers, projects, and data deliveries
  • Turn messy real-world signals into models, metrics, and product improvements
  • Partner with engineers, domain experts, and operators to improve how high-quality data is created and reviewed
  • Own high-impact systems from early design through production deployment
Required Qualifications
  • 3-6 YOE with relevant experiences
  • Strong software engineering background with experience shipping production systems
  • Experience with applied ML, ranking, recommendations, search quality, marketplace systems, trust/safety, fraud, or data quality systems
  • Strong data intuition and ability to work with messy, ambiguous real-world signals
  • Comfort working across backend systems, data pipelines, ML models, and internal tools
  • Ability to move quickly in a high-ownership, fast-changing environment
  • Deep care for quality, precision, and customer impact
Not a Fit If
  • You want to do pure research without owning production systems
  • You only want to train models and not build product infrastructure
  • You need clean datasets and perfectly scoped problems
  • You do not want to work closely with users, operators, and domain experts

Apply