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Research Machine Learning Federated Learning Jobs in Portland, OR

Deegit Inc

DSP Algorithms Engineer

Hillsboro, OR · On-site

$155K - $181K/yr

... research including ASR, echo cancelation, feedback cancellation, ANC, vocal pitch shifting and transformation. Proficient in DSP theory, scientific programming, adaptive filtering, machine learning ...

Heritage Specialty Group

Machine Operator

Milwaukie, OR · On-site

$19 - $21/hr

Operators will be responsible for maintaining a steady, efficient flow of product through the fill machine, learning the machine's interface fluently and the general well-being of the machine itself.

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All JobsResearch Machine Learning Federated Learning JobsResearch Machine Learning Federated Learning Jobs in Portland, OR

Research Machine Learning Federated Learning information

See Portland, OR salary details

$27K

$45.2K

$93.3K

How much do research machine learning federated learning jobs pay per year?

As of Jul 15, 2026, the average yearly pay for research machine learning federated learning in Portland, OR is $45,160.00, according to ZipRecruiter salary data. Most workers in this role earn between $34,500.00 and $48,800.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Researcher in Machine Learning Federated Learning, and why are they important?

To thrive as a Researcher in Machine Learning Federated Learning, you need a strong background in computer science, mathematics, and machine learning, typically supported by a relevant advanced degree (e.g., PhD or MSc). Familiarity with Python, TensorFlow, PyTorch, and distributed computing frameworks, as well as knowledge of privacy-preserving techniques and relevant research publications, is essential. Excellent analytical thinking, problem-solving abilities, and clear scientific communication are key soft skills for success in collaborative research environments. These competencies are vital to drive innovation, rigorously evaluate federated learning approaches, and advance privacy-preserving AI technologies.

What are some common challenges faced when implementing federated learning in a research environment?

One of the primary challenges in research-focused federated learning roles is ensuring data privacy and security while maintaining model performance across distributed devices. Researchers must also address issues such as handling heterogeneous data sources, communication bottlenecks between nodes, and the complexity of debugging decentralized systems. Collaborating with cross-functional teams—such as data engineers, privacy experts, and domain specialists—is vital to overcome these hurdles and drive successful outcomes. Staying updated with the latest advancements and actively contributing to open-source initiatives can also help researchers address these evolving challenges.

What is a Researcher in Machine Learning Federated Learning?

A Researcher in Machine Learning Federated Learning is a professional who investigates and develops methods to train machine learning models across multiple decentralized devices or servers, while keeping data localized and private. Their work focuses on improving algorithms, ensuring data privacy, and addressing challenges related to distributed learning, communication efficiency, and model accuracy. They often collaborate with other researchers, publish findings, and contribute to advancing technologies that make it possible to use sensitive data for AI without compromising privacy.

What is the difference between Research Machine Learning Federated Learning vs Data Scientist?

AspectResearch Machine Learning Federated LearningData Scientist
CredentialsAdvanced degrees in CS, ML, or related fields; research experienceBachelor's or Master's in Data Science, Statistics, or related fields
Work EnvironmentResearch labs, academic institutions, tech companies focusing on privacy-preserving MLBusiness environments, analytics teams, data-driven departments
Industry UsageDeveloping federated algorithms, privacy-preserving ML modelsData analysis, modeling, reporting, and insights generation

Research Machine Learning Federated Learning specialists focus on developing privacy-preserving algorithms across distributed data sources, often in research or R&D settings. Data Scientists analyze and interpret data to inform business decisions. While both roles require strong ML knowledge, federated learning roles emphasize distributed systems and privacy, whereas Data Scientists focus on data analysis and visualization.

What are popular job titles related to Research Machine Learning Federated Learning jobs in Portland, OR? For Research Machine Learning Federated Learning jobs in Portland, OR, the most frequently searched job titles are:
What job categories do people searching Research Machine Learning Federated Learning jobs in Portland, OR look for? The top searched job categories for Research Machine Learning Federated Learning jobs in Portland, OR are:
Research Machine Learning Federated Learning jobs near you
AI & Machine Learning Developer

AI & Machine Learning Developer

Johnson Health Tech North America Inc

Vancouver, WA

$99K - $119K/yr

Other

Medical, Dental, Life, Retirement, PTO

Re-posted 6 days ago

Johnson Health Tech rating

8.1

Company rating: 8.1 out of 10

Based on 5 frontline employees who took The Breakroom Quiz

132nd of 430 rated machine equipment manufacturers

Job description

Description


Position Overview:

Under the direction of the Sr. Director of Electrical Engineering, the AI/ML Developer - Mobile Fitness Applications will The AI/ML Developer will design and prototype advanced artificial intelligence features for Johnson Health Tech's mobile fitness applications. This role focuses on leveraging Large Language Models (LLMs) and AWS backend services to create innovative, personalized user experiences. The developer will collaborate closely with client-side Android developers to integrate these features into production applications.


Responsibilities:

Research, design, and implement AI/ML solutions for mobile fitness applications.

Develop and fine-tune LLMs for natural language interactions and personalization.

Build scalable backend services using AWS technologies (Lambda, DynamoDB, SageMaker, etc.).

Build scalable production ready ML ops pipeline and inference endpoints using AWS technologies e.g. SageMaker, Bedrock.

Collaborate with Android developers to integrate AI features into client-side applications.

Create prototypes and proof-of-concepts for new AI-driven features.

Stay current with emerging AI/ML technologies and best practices.

Ensure compliance with data privacy and security standards.

Requirements


Education: 

Bachelor's or Master's degree in Computer Science, Data Science, or a related field.


Experience:

3+ years of experience in AI/ML development, with a focus on Natural Language Processing (NLP) and LLMs.

Hands-on experience with AWS services for AI/ML deployment.

Proficiency in Python and ML frameworks (TensorFlow, PyTorch).

Experience with RESTful APIs and microservices architecture.


Benefits:

We offer an excellent compensation package and team-oriented work environment with growth opportunities. Some of our outstanding benefits include:

Health & Dental Insurance

Company paid Life Insurance

401(k)

Paid Time Off benefits

Product discounts

Wellness programs



 Equal Opportunity Employer, including Veterans and Individuals with Disabilities 

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