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Senior Machine Learning Engineer Jobs in Oregon (NOW HIRING)

OR · On-site

$104K - $143K/yr

Position Overview As a Senior Machine Learning Engineer at Anno.ai, you will design, develop, test, document, deploy, and maintain production machine learning and statistical modeled software to ...

Comscore, Total Visits, March 2025) Day to Day As a Senior Machine Learning Engineer on our Sourcing team, you will work on developing and deploying ML and AI solutions in production. You'll be ...

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 building cutting-edge AI products that directly impact how new therapies reach patients. We're looking for ...

Senior Machine Learning Engineer

OR · On-site +1

$205K - $270K/yr

Machine Learning Engineers at Cresta work across several high-impact AI initiatives. Final team placement is determined based on experience, strengths, and business needs. Current focus areas include:

OR

$104K - $143K/yr

About the role We are looking for a Senior Machine Learning Engineer, Voice Experience to help build the next generation of AI-powered voice systems for the contact center. In this role, you will ...

OR

$122K - $161K/yr

... Engineering, Mathematics, or a related field. * 8+/7+ years of professional work experience after BS/MS applying machine learning to real-world problems, and crafting scalable and effective ML/AI ...

OR

$205K - $355K/yr

Finally, you will help build the foundational patterns that ML engineers will use for years to come as we ramp up our effort to introduce machine learning into our platform * Collect and gather ...

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 ...

OR · On-site

$122K - $161K/yr

Senior Machine Learning Engineer, Data & Intelligence Products AcuityMD is a software and data platform that accelerates access to medical technologies. We help MedTech companies understand how their ...

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 ...

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 ...

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 ...

As a Machine Learning Engineer, you will prepare datasets, train and optimize models, and maintain ... senior guidance * Excellent understanding of model evaluation techniques, feature engineering ...

Machine Learning Engineer / Data Scientist** to join our team, working on agent harness research and model fine tuning. This role sits at the intersection of research and engineering: the ideal ...

OR · On-site

$134K - $180K/yr

The Machine Learning Engineer will tackle challenging problems and create scalable machine learning systems and platforms that make an impact on millions of users. This role will work closely with ...

Description Tyto Athene is seeking a driven and adaptable Machine Learning Engineer to help shape the future of cybersecurity through automation and machine learning. This role is an opportunity to ...

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Showing results 1-20

Senior Machine Learning Engineer information

See Oregon salary details

$62.9K

$133.8K

$194K

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

As of Jun 19, 2026, the average yearly pay for senior machine learning engineer in Oregon is $133,807.00, according to ZipRecruiter salary data. Most workers in this role earn between $110,500.00 and $151,700.00 per year, depending on experience, location, and employer.

What are some common challenges Senior Machine Learning Engineers face when deploying models to production, and how can they be addressed?

Senior Machine Learning Engineers often encounter challenges related to model scalability, maintaining performance in real-world scenarios, and ensuring reliable integration with existing systems. Addressing these challenges typically involves thorough testing, implementing robust monitoring for model drift, and collaborating closely with DevOps and software engineering teams to streamline deployment pipelines. Staying updated on best practices in MLOps and adopting tools for automated deployment and monitoring can greatly improve the reliability and efficiency of production models.

What does a Senior Machine Learning Engineer do?

A Senior Machine Learning Engineer designs, develops, and implements machine learning models to solve complex problems. They are responsible for selecting appropriate algorithms, preprocessing data, and optimizing model performance. Additionally, they collaborate with data scientists, software engineers, and product teams to integrate machine learning solutions into production systems. Senior engineers also mentor junior team members and contribute to setting technical direction for machine learning projects.

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

To thrive as a Senior Machine Learning Engineer, you need advanced knowledge of machine learning algorithms, statistical modeling, and programming languages like Python or Java, typically supported by a degree in computer science or a related field. Experience with frameworks and tools such as TensorFlow, PyTorch, scikit-learn, and cloud platforms, as well as familiarity with version control and CI/CD systems, is essential. Strong problem-solving, communication, and leadership skills help you collaborate effectively and mentor junior team members. These capabilities are crucial for designing scalable ML solutions and driving impactful results within complex, dynamic projects.

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

AspectSenior Machine Learning EngineerData Scientist
Required CredentialsBachelor's/Master's in CS, ML, or related; experience with ML frameworksBachelor's/Master's in CS, Statistics, or related; strong analytical skills
Work EnvironmentDevelops and deploys ML models in production systemsAnalyzes data, builds models, and provides insights
Industry UsageTech, finance, healthcare, e-commerceResearch, finance, marketing, tech

While both roles require strong technical skills and knowledge of machine learning, Senior Machine Learning Engineers focus more on deploying scalable ML solutions in production environments, whereas Data Scientists primarily analyze data and develop models for insights. The roles often overlap but differ in their core responsibilities and focus areas.

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

Senior Machine Learning Engineer

G2 Venture Partners

Clackamas, OR

$109K - $150K/yr

Other

Medical, Dental, Vision, Retirement

Posted 5 days ago


Job description

Senior Machine Learning Engineer

We are seeking a Senior Machine Learning Engineer to support our Public Sector initiatives focused on building and optimizing production ready AI systems for secure and distributed environments.

You will be responsible for transforming prototype models into scalable, efficient, and reliable production systems that operate seamlessly across a spectrum of hardware from government cloud infrastructure to edge devices in restricted or disconnected environments.

Responsibilities:

  • Design, develop, and deploy agentic workflows to orchestrate multi-step reasoning, tool use, and decision-making across production systems.
  • Productionize AI models from research prototypes into scalable, deployable systems used in real world applications.
  • Engineer adaptive ML systems using LoRA, PEFT, and on-device inference strategies, leveraging PyTorch, TensorFlow, and Hugging Face Transformers for model development, fine-tuning, and optimization.
  • Implement model optimization techniques such as quantization, pruning, distillation, and hardware specific acceleration.
  • Build and maintain Retrieval Augmented Generation (RAG) pipelines, including vector database integration for contextual retrieval.
  • Work with multi-modal AI systems across computer vision, audio, and natural language domains.
  • Optimize model execution for distributed and resource constrained environments, ensuring reliability under variable connectivity conditions.

Qualifications:

  • Active US Security clearance
  • 4+ years of experience in applied AI, ML engineering, or production AI systems.
  • Deep proficiency in PyTorch, TensorFlow, or Hugging Face Transformers.
  • Proven experience deploying AI models across cloud, edge, and mobile hardware environments.
  • Expertise in model compression and optimization (quantization, pruning, distillation).
  • Experience building RAG pipelines and integrating vector databases (e.g., Quadrant, ChromaDB, FAISS, Milvus, Pinecone).
  • Familiarity with multi-modal models and synthetic data generation methods.
  • Strong algorithmic and problem solving skills, especially in distributed or constrained compute environments.

Preferred Skills:

  • Experience with edge AI, federated learning, or offline inference systems.
  • Understanding of AI governance and compliance frameworks relevant to public sector deployments.
  • Experience integrating models into large scale distributed systems or microservice architectures.
  • Excellent communication and technical documentation skills for collaboration across multi disciplinary teams.
  • Strong understanding of GPU computing, CUDA, and performance profiling.

We at webAI are committed to living out the core values we have put in place as the foundation on which we operate as a team. We seek individuals who exemplify the following:

  • Truth - Emphasizing transparency and honesty in every interaction and decision.
  • Ownership - Taking full responsibility for one's actions and decisions, demonstrating commitment to the success of our clients.
  • Tenacity - Persisting in the face of challenges and setbacks, continually striving for excellence and improvement.
  • Humility - Maintaining a respectful and learning-oriented mindset, acknowledging the strengths and contributions of others.

Benefits:

  • Competitive salary
  • Comprehensive health, dental, and vision benefits package
  • 401(k) match (U.S.-based employees only)
  • $200/month Health & Wellness stipend
  • Continuing Education support
  • $500/year Function Health subscription (U.S.-based employees only)
  • Free parking for in-office employees
  • Flexible Time Off (FTO)
  • Parental leave for eligible employees
  • Supplemental life insurance

webAI is an Equal Opportunity Employer and does not discriminate against any employee or applicant on the basis of age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. We adhere to these principles in all aspects of employment, including recruitment, hiring, training, compensation, promotion, benefits, social and recreational programs, and discipline. In addition, it is the policy of webAI to provide reasonable accommodation to qualified employees who have protected disabilities to the extent required by applicable laws, regulations and ordinances where a particular employee works.