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Machine Learning Engineer Biotech Jobs in Boise, ID

Micron

Gas Systems Engineer - O&M

Boise, ID · On-site

The engineer leverages data analytics, AI-enabled tools, and digital technologies to enhance ... Utilize AI, machine learning, and advanced analytics tools to identify trends, predict equipment ...

New

Micron Technology

Gas Systems Engineer - O&M

Boise, ID · On-site

The engineer leverages data analytics, AI-enabled tools, and digital technologies to enhance ... Utilize AI, machine learning, and advanced analytics tools to identify trends, predict equipment ...

VELOX

GTM Engineer (AI Sales & Engineering)

Boise, ID · On-site

The AI Developer will be responsible for streamlining AI integrations with several existing ... The ideal candidate will have extensive experience working with AI, machine learning, and large ...

Micron

Staff Engineer, CE CAD

Boise, ID · On-site

$106K - $267K/yr

Explore and apply generative AI and machine learning techniques to practical custom IC design and ... Strong programming skills in Python, experience developing maintainable technical software or ...

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Machine Learning Engineer Biotech information

See Boise, ID salary details

$30K

$122.6K

$184.2K

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

As of Sep 3, 2026, the average yearly pay for machine learning engineer biotech in Boise, ID is $122,557.00, according to ZipRecruiter salary data. Most workers in this role earn between $96,600.00 and $147,500.00 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 Boise, ID?

For Machine Learning Engineer Biotech jobs in Boise, ID, the most frequently searched job titles are:

What job categories do people searching Machine Learning Engineer Biotech jobs in Boise, ID look for?

The top searched job categories for Machine Learning Engineer Biotech jobs in Boise, ID are:

Machine Learning Engineer Biotech jobs near you

Product Yield & Analytics PYAi Engineer

Micron Technology, Inc

Boise, ID • On-site

$90 - $120/hr

Other

This job post has expired today. Applications are no longer accepted.

Micron Technology rating

8.6

Company rating: 8.6 out of 10

Based on 43 frontline employees who took The Breakroom Quiz

26th of 161 rated electronics manufacturers

Job description

Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever.

We are seeking a highly motivated Senior Data Science & Product Yield Analytics Engineer to join the Product Yield & Analytics (PYA) organization. This role focuses on applying data science, software engineering, and advanced analytics to semiconductor manufacturing data, with a strong emphasis on Improved Software for Defect Analysis (ESDA) and Electrical Failure Analysis bench testing systems used companywide.

In this position, you will employ techniques drawn from statistics, machine learning, semiconductor physics, and large-scale data engineering to uncover patterns, build predictive models, and develop actionable insights that drive yield, quality, and reliability improvements across Micron’s memory products.

You will work closely with Data Scientists, Data Engineers, Product Engineers, Build teams, Yield Enhancement, RDA, Failure Analysis, and IT teams across global sites to develop and expand ESDA capabilities and analytics platforms that directly support manufacturing and product development.

Responsibilities:
  • Expand and enhance ESDA capabilities to improve yield, quality, and product reliability.
  • Develop yield, defect matching, and modeling methods to accelerate manufacturing feedback to RDA and YE teams.
  • Create and maintain engineering software tools for ESDA analytics and product line monitoring.
  • Collaborate with design, failure analysis, and product teams to optimize redundancy, compression, and defect analysis methodologies.
  • Support next-generation memory devices through advanced analytics and data-driven insights.
  • Using AI in applicable job functions.
Minimum Qualifications:
  • BS or MS in Electrical Engineering, Computer Engineering, Data Science, Software Development, or a related technical field.
  • Proficiency in at least one programming language and familiarity with machine learning and data science techniques.
  • Familiarity with Micron’s Improved Software for Defect Analysis (ESDA) system.
Preferred Qualifications:
  • Experience with memory products in product engineering, test, build, yield improvement, software development, or data science.
  • Strong analytical and problem-solving skills applied to sophisticated manufacturing data.
  • Ability to communicate complex technical concepts effectively across global, multi-functional teams.

Job Profile(s): Product Development Engineer 3 - Product Development Engineer 4

Relocation Level: TBD

Micron is proud to be an equal opportunity workplace and is an affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, age, national origin, citizenship status, disability, protected veteran status, gender identity or any other factor protected by applicable federal, state, or local laws.

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