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Mlops Machine Learning Engineer Jobs in Portland, OR

Pwc

AI Solutions Engineering Delivery Lead

Portland, OR · On-site

$108K - $143K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Intel

Senior AI / Data Science Engineer

Hillsboro, OR

$115K - $156K/yr

  • Medical

  • Retirement

  • PTO

Lead development of advanced AI, Machine Learning, and Generative AI solutions that address ... Collaborate closely with Yield Engineering, Process Integration, Manufacturing, and Technology ...

Pwc

CTIO AI Engineering Manager

Portland, OR

$73K - $244K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

A-dec, Inc.

AI Engineer, Sr

Newberg, OR

$109K - $150K/yr

Build and maintain machine learning models and AI services used in production environments * Design ... Familiarity with MLOps practices including model monitoring, versioning, and lifecycle management

A-dec, Inc.

AI Engineer, Sr

Newberg, OR · On-site

$109K - $150K/yr

Build and maintain machine learning models and AI services used in production environments * Design ... Familiarity with MLOps practices including model monitoring, versioning, and lifecycle management

Pwc

NGA AI Engineer Manager

Portland, OR

$73K - $244K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Pwc

US Tech - AI Engineering Senior Associate

Portland, OR · On-site

$55K - $187K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Pwc

ERP AI Engineer - Manager

Portland, OR

$99K - $232K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

... Machine Learning. Enhancing your leadership style, you motivate, develop and inspire others to ... with MLOps tooling and CI/CD pipelines for ML - Experience with vector databases and semantic ...

Deloitte

Lead AI and Data Science Engineer II

Portland, OR · On-site

$108K - $143K/yr

Data science applies statistical analysis, machine learning, and AI to identify meaningful patterns ... Lead AI and Data Science Engineer II Drive the design and delivery of advanced analytics ...

Pwc

US Tech - AI Engineering Manager

Portland, OR · On-site

$73K - $244K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

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Showing results 41-60

All JobsMlops Machine Learning Engineer JobsMlops Machine Learning Engineer Jobs in Portland, OR

Mlops Machine Learning Engineer information

See Portland, OR salary details

$33.4K

$136.6K

$205.2K

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

As of Aug 14, 2026, the average yearly pay for mlops machine learning engineer 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 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.

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 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 the key skills and qualifications needed to thrive as an MLOps machine learning engineer?

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.

What are popular job titles related to Mlops Machine Learning Engineer jobs in Portland, OR?

For Mlops Machine Learning Engineer jobs in Portland, OR, the most frequently searched job titles are:

What job categories do people searching Mlops Machine Learning Engineer jobs in Portland, OR look for?

The top searched job categories for Mlops Machine Learning Engineer jobs in Portland, OR are:

Mlops Machine Learning Engineer jobs near you

AI Scientist Senior II

Cambia Health Solutions

Portland, OR • On-site

Full-time

Re-posted 3 days ago

Cambia Health Solutions rating

8.4

Company rating: 8.4 out of 10

Based on 32 frontline employees who took The Breakroom Quiz

117th of 307 rated insurance

Job description

AI Scientist Senior II
Hybrid role (3 days/week in office) at our Burlington, Renton, Spokane, Vancouver, Portland, Medford, Salt Lake City, Boise, Lewiston, or Fargo offices.
Candidates must reside within commutable distance of that location or be willing to relocate.
Build a career with purpose. Join our Cause to create a person-focused and economically sustainable health care system.
Who We Are Looking For:
Every day, Cambia's Applied AI Team is living our mission to make health care easier and lives better. AI Scientists work with various stakeholders to design, develop, and implement data-driven solutions. This position applies deep expertise in advanced analytical tools such as generative AI, machine learning, deep learning, optimization, and statistical modeling to solve complex, high-impact business problems in the healthcare payer domain.
As a Senior II AI Scientist, you will serve as a technical leader and strategic advisor, driving innovation across multiple business areas such as clinical care delivery, customer experience, and payment integrity. This is a hands-on technical leadership role - you will personally architect and build sophisticated AI solutions while simultaneously mentoring junior team members and influencing the technical direction of our AI initiatives. You will lead by example, writing production-quality code, conducting rigorous experiments, and demonstrating best practices in every aspect of AI development. This role requires not only mastery of generative AI, machine learning, and deep learning, but also strong architectural thinking, advanced software engineering capabilities, and the ability to translate ambiguous business challenges into innovative AI solutions.
You will be expected to remain deeply technical, actively contributing code, developing models, and solving complex technical problems alongside your team. Your leadership will come through the quality of your technical work, your ability to tackle the most challenging problems, and your commitment to elevating the skills and capabilities of those around you.
AI Scientists work closely with AI team members in the Product and Engineering tracks to collaboratively develop and deliver models and data-driven products. At the Senior II level, you will lead cross-functional initiatives, establish best practices through your own exemplary work, and serve as a subject matter expert to both technical and business stakeholders - all in service of making our members' health journeys easier.
If you're an accomplished AI Scientist with a proven track record of delivering impactful solutions through hands-on technical excellence and leading others through example in the healthcare industry, apply for this exciting opportunity today!
What You Bring to Cambia:
Qualifications:
The AI Scientist Sr II would have a degree (masters or PhD preferred) in a strongly a strongly quantitative field such as Computer Science, Statistics, Applied Mathematics, Physics, Operations Research, Bioinformatics, or Econometrics, and typically at least 12 years of related work experience. Equivalent combination of education and experience will be considered.
Skills and Attributes:
Technical Leadership & Strategy
  • Recognized expert in generative AI, machine learning, and data science with ability to architect complex, novel solutions and define technical vision aligned with business strategy
  • Deep understanding of the healthcare industry (preferred) with ability to identify and prioritize high-value AI opportunities, evaluate emerging technologies, and lead multiple complex projects from conception to production

Advanced Technical Expertise:
  • Mastery of advanced AI/ML techniques with ability to innovate beyond existing patterns, combined with expert-level Python programming and strong software engineering principles (design patterns, testing, CI/CD)
  • Deep expertise in working with complex, real-world data challenges (noisy, high-dimensional, sparse, imbalanced, biased) across multiple data domains (e.g., claims, clinical, member engagement)
  • Deep expertise in multiple AI modeling techniques with ability to select and combine methods innovatively, design scalable architectures for offline and online systems, and implement MLOps, model governance, and responsible AI practices
  • Advanced SQL and data engineering skills, including optimization of complex queries and data pipeline design

Problem Solving & Innovation:
  • Ability to tackle ambiguous, ill-defined problems and structure them into actionable AI initiatives that create measurable business value
  • Proactive identification of AI opportunities for strategic advantage, with ability to anticipate technical risks, design mitigation strategies, and conduct research and experimentation including A/B testing and causal inference

Leadership & Collaboration:
  • Proven ability to mentor and develop junior AI Scientists while establishing and evangelizing best practices, coding standards, and technical processes
  • Strong leadership presence with ability to influence technical decisions across the organization, lead cross-functional teams, manage stakeholder relationships, and build productive partnerships across departments
  • Excellent communication skills with ability to present complex technical concepts to audiences ranging from technical teams to C-level executives

Business Acumen:
  • Strong ability to translate business strategy into AI opportunities and technical requirements, quantify business impact and ROI of AI initiatives, and balance technical excellence with pragmatic business delivery
  • Understanding of healthcare payer operations, regulations, and industry trends

Core Knowledge:
Generative AI
  • Foundation Models & Architectures: Deep understanding of transformer architectures, attention mechanisms, scaling laws, and experience with multiple model families (GPT, BERT, etc.)
  • Advanced Fine-tuning & Prompt Engineering: Expertise in parameter-efficient fine-tuning (LoRA, QLoRA, Adapters), instruction tuning, domain adaptation, and advanced prompting techniques (chain-of-thought, tree-of-thought, meta-prompting)
  • RAG & Agent Systems: Advanced RAG architectures, hybrid search strategies, knowledge base optimization, and experience designing AI agent systems with tool use, planning, and multi-agent collaboration
  • Evaluation, Alignment & Production: Deep expertise in evaluation methodologies (automated metrics, LLM-as-judge, human evaluation), alignment techniques (RLHF, DPO, constitutional AI), inference optimization, caching strategies, and cost management
  • Multimodal & Responsible AI: Experience with vision-language models and multimodal understanding, plus deep understanding of bias detection and mitigation, hallucination reduction, safety considerations, and privacy-preserving techniques
  • Frameworks & Tools: Expert-level proficiency with Hugging Face ecosystem, LangChain, LlamaIndex, vector databases, and emerging GenAI tools

Machine Learning
  • Advanced Algorithms & Methods: Deep expertise across supervised, unsupervised, semi-supervised, and reinforcement learning paradigms, including ensemble methods (boosting, bagging, stacking), time series forecasting, and causal inference
  • Optimization & Evaluation: Deep understanding of optimization algorithms, convergence properties, custom loss function design, experimental design, statistical testing, and bias-variance tradeoff analysis
  • AutoML & Transfer Learning: Experience with automated model selection, hyperparameter optimization at scale, and advanced techniques for knowledge transfer and few-shot learning

Deep Learning
  • Advanced Architectures & Optimization: Deep understanding of CNNs, RNNs, LSTMs, Transformers, GANs, VAEs, and diffusion models, plus advanced optimization methods, learning rate scheduling, and convergence analysis
  • Regularization & Specialized Domains: Advanced techniques including dropout variants, batch normalization, layer normalization, and architectural regularization, with expertise in NLP, computer vision, or speech processing
  • Model Compression: Knowledge of quantization, pruning, distillation, and efficient inference techniques

Mathematics
  • Core Mathematical Foundations: Advanced linear algebra (matrix decompositions, eigen analysis, numerical methods), probability and statistics (Bayesian methods, hypothesis testing, experimental design), optimization theory (convex optimization, constrained optimization, stochastic optimization), and information theory

Data & Software Engineering
  • Data Architecture & SQL: Understanding of data warehousing, data lakes, modern data stack components, plus advanced SQL including query optimization, window functions, CTEs, and performance tuning
  • Software Engineering & MLOps: Design patterns, testing strategies (unit, integration, end-to-end), version control, CI/CD, model versioning, experiment tracking, model monitoring, and deployment strategies
  • Distributed Computing & Cloud: Experience with distributed training, data parallelism, scalable data processing (Spark, Dask, Ray), and proficiency with cloud AI/ML services (AWS SageMaker, Azure ML, GCP Vertex AI)

What you will do at Cambia:
Note: At the Senior II level, you are expected to demonstrate significant initiative, innovation, and leadership beyond core technical execution. You will shape technical direction, mentor others, and drive strategic AI initiatives.
Technical Leadership & Architecture
  • Lead the design and architecture of complex, multi-component AI systems that solve strategic business problems, while defining technical standards, best practices, and design patterns for AI development across the team
  • Evaluate and recommend new AI technologies, frameworks, and methodologies for adoption, serving as the technical authority on AI/ML topics
  • Drive innovation by researching and prototyping cutting-edge AI techniques applicable to healthcare challenges, and lead technical design reviews to ensure high-quality solutions

Advanced Model Development & Innovation
  • Research, design, and implement novel AI solutions using state-of-the-art generative AI, machine learning, and deep learning techniques to handle complex, real-world healthcare data challenges
  • Design custom algorithms and modeling approaches when existing solutions are insufficient, and develop advanced evaluation frameworks that capture business value and model behavior
  • Create reusable components, libraries, and frameworks that accelerate AI development, and lead the development of production grade AI systems with robust monitoring, governance, and maintenance strategies

Strategic Problem Solving & Business Impact
  • Partner with business leaders to identify high-impact AI opportunities and translate ambiguous business challenges into well-defined AI problems with clear success criteria
  • Design comprehensive experimentation strategies including A/B testing, causal inference, and statistical validation
  • Proactively identify risks, biases, and ethical considerations in AI solutions and develop mitigation strategies, while quantifying and communicating business impact and ROI to executive stakeholders

Data & Engineering Excellence
  • Design and optimize complex data pipelines for model training, evaluation, and serving, while developing advanced feature engineering strategies that unlock model performance
  • Build scalable, maintainable AI systems using modern MLOps practices and cloud infrastructure, with comprehensive monitoring and observability for production systems
  • Ensure data quality, governance, and compliance with healthcare regulations (HIPAA, etc.)

Mentorship & Team Development
  • Mentor junior and mid-level AI Scientists, providing technical guidance and career development support through code reviews and constructive feedback
  • Lead knowledge-sharing sessions, workshops, and technical presentations, while contributing to hiring and onboarding processes
  • Foster a culture of continuous learning, experimentation, and technical excellence

Cross-Functional Collaboration & Communication
  • Lead cross-functional initiatives involving Product, Engineering, and Business stakeholders, communicating complex technical concepts effectively to both technical and non-technical audiences, including executives
  • Build strong partnerships across the organization to identify opportunities and remove blockers, represent the AI team in strategic planning and roadmap discussions, and contribute to thought leadership through presentations, publications, or industry engagement

Responsible AI & Governance
  • Champion responsible AI practices including fairness, transparency, and accountability, while developing frameworks for bias detection, mitigation, and ongoing monitoring
  • Ensure AI solutions comply with regulatory requirements and ethical guidelines, and lead efforts to document model decisions, assumptions, and limitations for governance purposes

Pay ranges vary based on the candidate's work location. The expected hiring range depends on skills, experience, education, and training; relevant licensure / certifications; and performance history.
  • Oregon, Washington, Utah, and Idaho: The expected hiring range is $168,000 - $211,000, the full salary range is $168,000 - $275,000, and the bonus target is 20%.

  • North Dakota: The expected hiring range is $148K - $196K, and the full salary range is $148K - $244K.

About Cambia
Working at Cambia means being part of a purpose-driven, award-winning cul

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