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Research Machine Learning Federated Learning Jobs in Oregon

Instacart

OR

Candidates should bring some relevant research experience, typically in computationally intensive empirical topics, as well as some exposure to machine learning coursework and applications. The ...

Instacart

OR

Machine learning is central to how we build intelligent shopping experiences at Instacart. We use ... Researching techniques to deploy LLMs in high-traffic, latency-sensitive production environments ...

Netflix

OR

$523K - $920K/yr

We are seeking an experienced Machine Learning leader to lead a team of Research Scientists and Machine Learning Engineers working on multimodal LLM and audio algorithms. You will support a highly ...

Tyto Athene, LLC

Machine Learning Engineer

Serve as a subject matter expert in Machine Learning and its applications in Cyber Defense, researching and implementing differentiating and novel ML-based solutions to problems SOCs face.

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 ...

Intel

Machine Learning Engineer

Hillsboro, OR

This role sits at the intersection of research and engineering: the ideal candidate designs and ... machine learning engineering, data science or ML research * Proficient in Python * Proficient in ...

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 ...

Vectara

Machine Learning (ML) Engineer

OR · On-site +1

Design, prototype, research and build AI systems for Vectara. * Train, evaluate and deploy ML ... machine learning to real-world problems, and crafting scalable and effective ML/AI solutions.

Varsity Tutors

Machine Learning Tutor

Eugene, OR · Remote

$40/hr

About the Job The Varsity Tutors Live Learning Platform has thousands of students looking for online Machine Learning tutors nationally. As a tutor on the Varsity Tutors Platform, you'll have the ...

Varsity Tutors

Machine Learning Tutor

OR · Remote

$40/hr

About the Job The Varsity Tutors Live Learning Platform has thousands of students looking for online Machine Learning tutors nationally. As a tutor on the Varsity Tutors Platform, you'll have the ...

Varsity Tutors

Machine Learning Tutor

Portland, OR · Remote

$40/hr

About the Job The Varsity Tutors Live Learning Platform has thousands of students looking for online Machine Learning tutors nationally. As a tutor on the Varsity Tutors Platform, you'll have the ...

Indeed

Machine Learning Engineer

Portland, OR

As a Machine Learning Engineer, you will prepare datasets, train and optimize models, and maintain ... You will learn and apply new techniques from open source packages and research publications, and ...

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Research Machine Learning Federated Learning information

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

Senior Machine Learning Engineer

G2 Venture Partners

Clackamas, OR • On-site

$109K - $150K/yr

Other

Medical, Dental, Vision, Retirement

Posted 7 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.

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