ZipRecruiter

Log In

1

Machine Learning Engineer Biotech Jobs (NOW HIRING)

Advatix Inc.

Machine Learning Engineer

San Mateo, CA · On-site

$110 - $165/hr

Machine Learning Engineer / Research Engineer Pay: $$110,000 - $165,000 Base Salary + Equity Shift: N/A Location: San Mateo, CA (Peninsula) - Onsite Preferred Schedule: Full time, Permanent Role Visa ...

AI Squared

Machine Learning Engineer

Washington, DC · On-site

Machine Learning Engineer Washington, DC (Hybrid) About the Role: We are seeking a highly skilled Machine Learning Engineer to join our core AI team. In this role, you will focus on deploying ...

New York Times

Machine Learning Engineer

Manhattan, NY · On-site

$170.17 - $255.26/hr

Job Overview Machine Learning Engineer w/ Spotify USA Inc. in NY, NY. Bld productn systms that enrich & improve Spotify listeners' exp on Spotify usg machine learng techniques. Bach deg (U.S. or for ...

Tapestry

Machine Learning Engineer

Mountain View, CA · On-site

About the role: We're looking for an early career Machine Learning Engineer to join our team. In this role you will build and deploy state of the art machine learning models to solve complex ...

previous pagenext page

Showing results 21-40

All JobsTechnology JobsData JobsData Scientist JobsMachine Learning Engineer Biotech Jobs

Machine Learning Engineer Biotech information

See salary details

$31.5K

$128.8K

$193.5K

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

As of Aug 22, 2026, the average yearly pay for machine learning engineer biotech in the United States is $128,769.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,500.00 and $155,000.00 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.

More about Machine Learning Engineer Biotech jobs

What cities are hiring for Machine Learning Engineer Biotech jobs?

Cities with the most Machine Learning Engineer Biotech job openings:

What are the most commonly searched types of Machine Learning Engineer Biotech jobs?

The most popular types of Machine Learning Engineer Biotech jobs are:

What states have the most Machine Learning Engineer Biotech jobs?

States with the most job openings for Machine Learning Engineer Biotech jobs include:

What job categories do people searching Machine Learning Engineer Biotech jobs look for?

The top searched job categories for Machine Learning Engineer Biotech jobs are:

Machine Learning Engineer Biotech jobs near you
Infographic showing various Machine Learning Engineer Biotech job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $128,769 per year, or $61.9 per hour.

Machine Learning Engineer

Happy Elements

San Francisco, CA

Full-time

Re-posted 14 days ago

Job description

Machine Learning Engineer
Full-time
Responsibilities
  • Build, maintain, and improve efficient and reliable data mining and machine learning models.
  • Design, implement and tune machine learning models, and provide performance feedback.
  • Work closely with data engineers to adapt and improve data pipelines for production models.
  • Work closely with software engineers in putting models into production (interface, SLA, scalability).Qualifications
  • Strong academic background required. MS in Computer Science or Machine Learning with 2+ years of industry experience or PhD in related field with 1+ years of industry experience required.
  • Expert in Python, and computation graph toolkits (e.g., Scikit-learn, Tensorflow). Solid experience with Python packages such as Numpy, Panda, and Scikit-learn.
  • Expert/Master in common families of machine learning models, feature engineering, feature selection techniques, and tuning of machine learning models.
  • Master with SQL or other relational database.
  • Master in building and productionizing end-to-end machine learning systems.
  • Knowledge and experience in cloud computing is a plus.
  • Extensive data modeling and data architecture skills.
  • Advanced math skills (linear algebra, Bayesian statistics, group theory).
  • Ability to consistently exercise independent discretion and judgment on significant matters.
  • Strong analytical, problem-solving and communication skills.
  • Ability to work in a team environment

Apply