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Machine Learning Engineer Biotech Jobs in Oakville, CA

PhD in Machine Learning, Computer Science, Computational Biology, Bioinformatics, Statistics ... Strong programming skills using modern ML frameworks (PyTorch, JAX, etc.) * Experience with ...

PhD in Machine Learning, Computer Science, Computational Biology, Bioinformatics, Statistics ... Strong programming skills using modern ML frameworks (PyTorch, JAX, etc.) * Experience with ...

AI ENGINEER

Windsor, CA · On-site

$120 - $180/hr

Design, build, and deploy scalable machine learning solutions using Gemini Enterprise and Google ... Collaborate with data scientists, engineers, and business stakeholders to deliver production-ready ...

New

They're hiring early-career engineers with raw ability rather than domain experience ... The role is deliberately broad: machine learning, geometry, graphics, and AI, spanning geometry and ...

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Showing results 1-20

Machine Learning Engineer Biotech information

See Oakville, CA salary details

$36K

$147.3K

$221.4K

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

As of Sep 6, 2026, the average yearly pay for machine learning engineer biotech in Oakville, CA is $147,317.00, according to ZipRecruiter salary data. Most workers in this role earn between $116,100.00 and $177,300.00 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 cities near Oakville, CA are hiring for Machine Learning Engineer Biotech jobs?

Cities near Oakville, CA with the most Machine Learning Engineer Biotech job openings:

Infographic showing various Machine Learning Engineer Biotech job openings in Oakville, CA as of August 2026, with employment types broken down into 1% As Needed, 72% Full Time, 23% Part Time, 1% Temporary, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $147,317 per year, or $70.8 per hour.

Machine Learning Engineer

Keysight Technologies, Inc.

Santa Rosa, CA • On-site

$108K - $180K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 4 days ago


Key responsibilities

  • Partner with RF measurement and post-silicon teams to translate calibration workflows, de-embedding challenges, and characterization requirements into ML-ready formulations.

  • Develop ML models for adaptive calibration, intelligent de-embedding, and fixture characterization, including Transformers and Vision Models.

  • Write production-ready Python, C++, and CUDA code and integrate with instrument control frameworks.


Keysight Technologies rating

8.1

Company rating: 8.1 out of 10

Based on 20 frontline employees who took The Breakroom Quiz

52nd of 161 rated electronics manufacturers


Job description

Overview

Keysight is at the forefront of technology innovation, delivering breakthroughs and trusted insights in electronic design, simulation, prototyping, test, manufacturing, and optimization. Our ~16,800 employees create world-class solutions in communications, 5G, automotive, energy, quantum, aerospace, defense, and semiconductor markets for customers in over 100 countries. Learn more about what we do.

Our award-winning culture embraces a bold vision of where technology can take us and a passion for tackling challenging problems with industry-first solutions. We believe that when people feel a sense of belonging, they can be more creative, innovative, and thrive at all points in their careers.


Responsibilities
  • Partner with RF measurement and post-silicon teams to translate calibration workflows, de-embedding challenges, and characterization requirements into ML-ready formulations.
  • Develop ML models for adaptive calibration, intelligent de-embedding, and fixture characterization — including Transformers for frequency-domain signal analysis and Vision Models for calibration artifact identification.
  • Apply Bayesian/GP optimization for adaptive calibration loop closure, impedance tuner control, and parametric yield optimization; RL (PPO, DDPG, SAC) for automated measurement sequencing and closed-loop RF tuning.
  • Build generative models for synthetic S-parameter dataset generation and calibration standard augmentation.
  • Write production-ready Python (scikit-rf, PyTorch), C++, and CUDA code; integrate with instrument control frameworks (SCPI, IVI, VISA).
  • Benchmark models against hardware measurements and physics-based calibration standards.

Qualifications
  • Pursuing PhD in EE (RF/Microwave), Applied Physics, CS, or related field.
  • Hands-on RF measurement experience: S-parameter characterization, calibration, and de-embedding.
  • Strong ML background with experience applying GNNs, Transformers, or Neural Operators to measurement or signal processing tasks.
  • Background in Bayesian optimization applied to hardware calibration or measurement tuning.
  • Familiarity with SCPI/IVI/PyVISA instrument control and RF measurement automation.

Desired Qualifications

  • Experience with advanced calibration techniques (multiline TRL, unknown thru, in-fixture, on-wafer probing).
  • Background in load-pull, noise figure, or linearity characterization (IP3, P1dB) for RF device optimization.
  • Familiarity with Keysight RF platforms (PNA, PNA-X, ENA) or simulation tools (ADS, RFPro).
  • Publications or patents in ML for RF measurement, smart calibration, or adaptive test.

Careers Privacy Statement
Keysight Technologies Inc. is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability or any other protected categories under all applicable laws.

The level of role and salary will be based on applicable experience, education and skills; Most offers will be between the minimum and the midpoint of the Salary Range listed below.

California Pay Range: MIN $108,350 - MAX $180,580

Note: For other locations, pay ranges will vary by region.

US Employees may be eligible for the following benefits:

- Medical, dental and vision

- Health Savings Account

- Health Care and Dependent Care Flexible Spending Accounts

- Life, Accident, Disability insurance

- Business Travel Accident and Business Travel Health

- 401(k) Plan

- Flexible Time Off, Paid Holidays

- Paid Family Leave

- Discounts, Perks

- Tuition Reimbursement

- Adoption Assistance

- ESPP (Employee Stock Purchase Plan)

Qualifications:
  • Pursuing PhD in EE (RF/Microwave), Applied Physics, CS, or related field.
  • Hands-on RF measurement experience: S-parameter characterization, calibration, and de-embedding.
  • Strong ML background with experience applying GNNs, Transformers, or Neural Operators to measurement or signal processing tasks.
  • Background in Bayesian optimization applied to hardware calibration or measurement tuning.
  • Familiarity with SCPI/IVI/PyVISA instrument control and RF measurement automation.

Desired Qualifications

  • Experience with advanced calibration techniques (multiline TRL, unknown thru, in-fixture, on-wafer probing).
  • Background in load-pull, noise figure, or linearity characterization (IP3, P1dB) for RF device optimization.
  • Familiarity with Keysight RF platforms (PNA, PNA-X, ENA) or simulation tools (ADS, RFPro).
  • Publications or patents in ML for RF measurement, smart calibration, or adaptive test.

Careers Privacy Statement
Keysight Technologies Inc. is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability or any other protected categories under all applicable laws.

The level of role and salary will be based on applicable experience, education and skills; Most offers will be between the minimum and the midpoint of the Salary Range listed below.

California Pay Range: MIN $108,350 - MAX $180,580

Note: For other locations, pay ranges will vary by region.

US Employees may be eligible for the following benefits:

- Medical, dental and vision

- Health Savings Account

- Health Care and Dependent Care Flexible Spending Accounts

- Life, Accident, Disability insurance

- Business Travel Accident and Business Travel Health

- 401(k) Plan

- Flexible Time Off, Paid Holidays

- Paid Family Leave

- Discounts, Perks

- Tuition Reimbursement

- Adoption Assistance

- ESPP (Employee Stock Purchase Plan)

Education:UNAVAILABLEEmployment Type: UNAVAILABLE

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