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Machine Learning Engineer Biotech Jobs in Portland, OR

Chabez Tech

Machine Learning Engineer

Portland, OR · On-site

Machine Learning Engineer Location: Portland, OR - Onsite (Local only / F2F interview) Duration: 24 Months Contract Experience Level: 5+ years of experience Required Qualifications • Bachelor's or ...

Chabez Tech

Machine Learning Engineer

Portland, OR

Machine Learning Engineer Location: Portland, OR - Onsite (Local only / F2F interview) Duration: 24 Months Contract Experience Level: 5+ years of experience Required Qualifications • Bachelor's or ...

Chabez Tech

Machine Learning Engineer

Portland, OR

Machine Learning Engineer Location: Portland, OR - Onsite (Local only / F2F interview) Duration: 24 Months Contract Experience Level: 5+ years of experience Required Qualifications Bachelor's or ...

Varsity Tutors

Machine Learning Tutor

Portland, OR · Remote

$40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

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Machine Learning Engineer Biotech information

See Portland, OR salary details

$33.4K

$136.6K

$205.2K

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

As of May 30, 2026, the average yearly pay for machine learning engineer biotech in Portland, OR is $136,560.00, according to ZipRecruiter salary data. Most workers in this role earn between $107,600.00 and $164,400.00 per year, depending on experience, location, and employer.

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.

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 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.

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 Portland, OR? For Machine Learning Engineer Biotech jobs in Portland, OR, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer Biotech jobs in Portland, OR look for? The top searched job categories for Machine Learning Engineer Biotech jobs in Portland, OR are:
What cities near Portland, OR are hiring for Machine Learning Engineer Biotech jobs? Cities near Portland, OR with the most Machine Learning Engineer Biotech job openings:
Machine Learning Engineer Biotech jobs near you

Machine Learning Engineer

Chabez Tech

Portland, OR • On-site

Contractor

Posted 5 days ago

Job description

Company Description
Job Description
Job Title: Machine Learning Engineer
Location: Portland, OR - Onsite (Local only / F2F interview)
Duration: 24 Months Contract
Experience Level: 5+ years of experience
Required Qualifications
• Bachelor's or master's degree in computer science, Machine Learning, Electrical Engineering, or related field
• 5+ years of experience in machine learning, data science, or AI engineering
• Strong programming skills in Python (NumPy, Pandas, scikit-learn, PyTorch/TensorFlow)
• Experience with time-series data analysis and anomaly detection
• Hands-on experience with causal inference methods (e.g., Bayesian networks, structural causal models)
• Experience building or working with knowledge graphs (Neo4j, RDF, graph databases)
• Understanding of explainable AI techniques (SHAP, LIME, counterfactual analysis)
• Experience deploying ML models in production systems
• Strong problem-solving skills and ability to work with complex, real-world datasets
Preferred Qualifications
• Experience with fault tree analysis (FTA), reliability engineering, or failure analysis
• Background in industrial systems, semiconductors, manufacturing, or IoT environments
• Experience with graph-based ML / Graph Neural Networks (GNNs)
• Familiarity with RCA methodologies (FMEA, 5 Whys, fishbone diagrams)
• Experience with vector databases, RAG systems, or LLM-based reasoning
• Knowledge of MLOps practices (CI/CD, monitoring, model governance)
• Experience working in air-gapped or high-security environments
Qualifications
Additional Information
All your information will be kept confidential according to EEO guidelines.

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