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Mlops Machine Learning Engineer Jobs in Oregon (NOW HIRING)

Chabez Tech

Machine Learning Engineer

Portland, OR

Job Title: Machine Learning Engineer Location: Portland, OR - Onsite (Local only / F2F interview ... of MLOps practices (CI/CD, monitoring, model governance) • Experience working in air-gapped or ...

Chabez Tech

Machine Learning Engineer

Portland, OR · On-site

Machine Learning Engineer Location: Portland, OR - Onsite (Local only / F2F interview) Duration: 24 ... of MLOps practices (CI/CD, monitoring, model governance) • Experience working in air-gapped or ...

Chabez Tech

Machine Learning Engineer

Portland, OR

Job Title: Machine Learning Engineer Location: Portland, OR - Onsite (Local only / F2F interview ... MLOps practices (CI/CD, monitoring, model governance) Experience working in air-gapped or high ...

OneStudyTeam

Senior Machine Learning Engineer

OR · On-site +1

$140K - $190K/yr

By joining our team as a Senior Machine Learning Engineer , you will play a pivotal role in ... Leverage modern cloud tools and MLOps best practices to build robust data pipelines and deploy ...

Automation Anywhere, Inc.

OR

... Machine Learning Engineer to help power the next generation of AI-driven digital agents ... Implementing MLOps best practices, automating model training, validation, deployment, and ...

Quantiphi, Inc.

OR

$114.40K - $137.40K/yr

We are looking for a Machine Learning Engineer with strong expertise in Google Cloud AI tools, ML ... Knowledge of MLOps practices for automating ML workflows, model versioning, and continuous ...

HealthEdge

Machine Learning Engineer

Overview Machine Learning Engineer, AI Platform As a Machine Learning Engineer, you will design, build, and ship AI agents and automation that solve real problems across HealthEdge's engineering ...

Workiva, Inc.

Staff Machine Learning Engineer

OR · On-site +1

Join our team at Workiva as a Staff Machine Learning Engineer! As a pivotal member of our Machine ... Architect and deliver cutting-edge ML solutions using MLOps and best practices, fostering ...

Quantiphi, Inc.

OR

$122.40K - $161.30K/yr

Senior Machine Learning Engineer Experience Level: 4+ years Work Location: Dallas, TX Employment ... MLOps & Production Systems: Performance Monitoring and End-to-End Tracing: Implement and manage ...

Cellanome

Machine Learning Engineer

Foster, OR · On-site +1

$160K - $215K/yr

The Machine Learning Engineer will work in close collaboration with the core instrument, assay and software teams to develop algorithms for data analysis and workflow automation. This role reports to ...

Upstart

OR

As a Principal Machine Learning Engineer, you will work at the intersection of applied ML and platform engineering-collaborating closely with Research Scientists, Data Scientists, and ML Platform ...

Autodesk

Senior Machine Learning Test Engineer

OR · On-site +1

$110.40K - $143.40K/yr

Job Requisition ID # 26WD98377 Senior Machine Learning Test Engineer Location: United States East ... Your skills span test strategy, automation, and a little MLOps, with a strong software engineering ...

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Mlops Machine Learning Engineer information

What are the key skills and qualifications needed to thrive as an MLOps Machine Learning Engineer, and why are they important?

To thrive as an MLOps Machine Learning Engineer, you need a strong background in machine learning concepts, software engineering, and cloud infrastructure, typically supported by a degree in computer science or a related field. Familiarity with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (AWS, GCP, Azure), and certifications such as Google Professional Machine Learning Engineer are highly beneficial. Strong problem-solving abilities, collaboration, and communication skills help you work effectively across data science and engineering teams. These skills are essential for reliably deploying, monitoring, and maintaining scalable machine learning solutions in production environments.

How does an MLOps Machine Learning Engineer typically collaborate with data scientists and software engineers during the deployment of machine learning models?

An MLOps Machine Learning Engineer acts as a bridge between data scientists and software engineers, ensuring machine learning models transition smoothly from development to production. They often work closely with data scientists to understand model requirements, data pipelines, and performance metrics, while also collaborating with software engineers to integrate models into scalable systems. Regular communication, shared documentation, and joint troubleshooting sessions are common, as the role requires aligning model performance with system reliability and maintainability. This collaborative environment helps ensure that models are robust, scalable, and impactful in real-world applications.

What does an MLOps Machine Learning Engineer do?

An MLOps Machine Learning Engineer bridges the gap between data science and IT operations by developing, deploying, and maintaining machine learning models in production environments. They are responsible for automating workflows, managing model versioning, monitoring performance, and ensuring scalability and reliability of ML systems. Their work enables organizations to deploy machine learning solutions efficiently and consistently, making it easier to update and manage models as business needs evolve.

What is the difference between Mlops Machine Learning Engineer vs Data Scientist?

AspectMlops Machine Learning EngineerData Scientist
Required CredentialsBachelor's or master's in CS, data science, or related fields; certifications in cloud platforms or MLOps toolsBachelor's or master's in statistics, data science, or related fields; certifications in data analysis or machine learning
Work EnvironmentFocus on deploying, maintaining, and scaling ML models in production environmentsFocus on data analysis, model development, and insights generation
Employer & Industry UsageTech companies, startups, enterprises implementing ML solutionsResearch institutions, analytics firms, tech companies for data insights

While both roles involve machine learning, Mlops Machine Learning Engineers specialize in deploying and maintaining models in production, ensuring scalability and reliability. Data Scientists primarily focus on developing models and analyzing data to generate insights. The roles often overlap but differ in their core responsibilities and work environments.

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

Machine Learning Engineer

Chabez Tech

Portland, OR

Contractor

Posted 5 days ago

Job 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 
 

Additional Information

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