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Research Machine Learning Federated Learning Jobs in Oregon

Gametime United

Head of Applied Machine Learning

OR · On-site +1

Position Summary Gametime is seeking a Head of Applied Machine Learning to lead the development and application of machine learning and LLM-powered models that drive meaningful business impact across ...

Quora

$139K - $168K/yr

At Poe, we use Machine Learning in various parts of the product - bot routing, agent flow, code editing, RAG, etc. Our team of Machine Learning Engineers have high impact by advancing the current ...

phData

OR · On-site

We are looking for a Principal Solutions Architect to join our Machine Learning team. In this role, you will lead the architecture, implementation, and lifecycle management of AI/ML applications that ...

Cotiviti

$125K - $172K/yr

This role sits at the intersection of applied research and production engineering, translating ... Advanced Statistics, Machine learning and AI. * 12+ years of industry experience building ...

Nextdoor

OR · On-site

$170K - $334K/yr

Machine learning is starting to transform our product through personalization, driving major impact across different parts of our platform including newsfeed, notifications, ads relevance ...

Nextdoor

OR · On-site

$205K - $355K/yr

Machine learning is starting to transform our product through personalization, driving major impact across different parts of our platform, including newsfeed, notifications, ad relevance ...

Aptive

Minimum Qualifications Minimum 8 years of professional experience in data science, machine learning ... differential privacy, federated learning) applied to protected health information (PHI) or ...

Whiterabbit.ai

Learn and understand a large body of research in deep learning and machine learning * Participate in cutting-edge research for medical applications of computer vision Must Have Experience

Netflix

OR · On-site

You will build the machine learning models that augment our simulation-based engine for predicting ... Experience applying GenAI to boost developer/research productivity. Generally, our compensation ...

Netflix

OR

$466K - $750K/yr

Design and implement machine learning and optimization algorithms to improve ad quality and performance. Build, train, and evaluate models on large-scale production data. Develop online and offline ...

Netflix

Machine Learning Scientist (L6) - Live Ads

OR · On-site +1

We are looking for a Machine Learning Scientist 6 to serve as a vertical technical lead across our core Live Ads ML problem areas - forecasting, targeting and personalization, bidding and pacing ...

Netflix

OR

$466K - $750K/yr

We are looking for a seasoned Machine Learning Scientist to design and develop innovative Machine ... Lead end-to-end ML development: research, model training, and evaluation Partner with ML scientists ...

Netflix

OR

$466K - $750K/yr

Design and implement machine learning-driven bidding algorithms that optimize ad performance against objectives such as Clicks, Conversions, CPA and ROAS,. Build, train, and evaluate bidding ...

Numerator

Numerator is looking for a hands-on Tech Lead Manager to join our growing Machine Learning team. This is a 50/50 player-coach role: you'll directly manage a small team of ML engineers while ...

Numerator

Numerator is looking for a hands-on Tech Lead Manager to join our growing Machine Learning team. This is a 50/50 player-coach role: you'll directly manage a small team of ML engineers while ...

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All JobsResearch Machine Learning Federated Learning JobsResearch Machine Learning Federated Learning Jobs in Oregon

Research Machine Learning Federated Learning information

What are the key skills and qualifications needed to thrive as a researcher in machine learning federated learning?

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

Head of Applied Machine Learning

Gametime United

OR • On-site, Remote

Full-time

Posted 4 days ago

Job description

Position Summary

Gametime is seeking a Head of Applied Machine Learning to lead the development and application of machine learning and LLM-powered models that drive meaningful business impact across product, marketing, operations, and other key functions. This role is ideal for a hands-on, applied ML leader who thrives at the intersection of modeling excellence and business understanding. You will work closely with Product, Data, Engineering, and business partners to identify high-value opportunities, translate them into well-defined modeling problems, and deliver production-ready solutions. A core focus of this role will be curation, including ranking, filtering, and personalization systems that directly shape the customer experience, alongside thoughtful application of modern LLM-based techniques.

Who You Are
  • An experienced applied ML practitioner with a track record of delivering production models that move business metrics
  • Deeply comfortable owning ranking, recommendation, and curation problems from framing through iteration in production
  • Experienced applying both classical ML techniques and LLM-based approaches with strong technical judgment
  • A player-coach who can review code, guide modeling decisions, and mentor ML practitioners
  • Business-oriented, seeking context, tradeoffs, and outcomes rather than purely technical elegance
  • Comfortable managing multiple initiatives across stakeholders and timelines
  • A clear communicator who can translate complex ML concepts into business-relevant insights
  • Curious and motivated to stay current with applied ML and LLM advancements
What You Will Work On

Applied ML and Business Alignment

  • Partner with Product, Marketing, Operations, and other teams to identify where ML can drive measurable value
  • Translate business problems into clear modeling objectives, metrics, and experimentation plans
  • Ensure ML efforts remain tightly aligned with business priorities and user impact

Ranking, Curation, and Personalization

  • Lead the design, development, and iteration of ranking, filtering, and personalization models across Gametime's product surfaces
  • Own modeling approaches, feature strategy, evaluation metrics, and offline and online experimentation
  • Balance relevance, revenue, and user trust when evolving ranking solutions

LLM and Advanced Modeling Applications

  • Apply LLMs and hybrid ML techniques to use cases such as semantic understanding, intent detection, content generation, and internal workflows
  • Evaluate emerging tools and techniques, recommending pragmatic adoption where they provide clear benefit
  • Establish best practices for testing, deploying, and monitoring LLM-powered models in production

Team Leadership and Craft Excellence

  • Manage and mentor applied ML practitioners, supporting growth in technical depth and business impact
  • Set high standards for modeling rigor, experimentation discipline, and production readiness
  • Collaborate closely with ML engineering and platform teams to ensure scalable and reliable deployment
Experience You Bring
  • Bachelor's degree in Computer Science, Engineering, or a related field (advanced degree preferred)
  • 6+ years of experience building and deploying production machine learning models
  • Demonstrated experience owning ranking, recommendation, or personalization systems
  • Strong foundation in applied ML techniques such as learning-to-rank, embeddings, gradient boosting, and neural networks
  • Hands-on experience working with LLMs, including prompt engineering, fine-tuning, retrieval-augmented generation, and evaluation
  • Solid software engineering skills and experience working within modern data and ML stacks
  • Proven ability to work cross-functionally and influence without relying on hierarchy
What Success Looks Like
  • Applied ML solutions that measurably improve customer experience and business outcomes
  • High-quality, continuously improving ranking and curation systems
  • Thoughtful, value-driven use of LLMs rather than novelty applications
  • Strong partnership with product and business teams, with ML viewed as a strategic enabler
  • A supported, high-performing applied ML team delivering consistent impact

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