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Senior Machine Learning Researcher Jobs in Oregon

Read eval signal and training curves to determine whether a change actually helped, and feed findings back to the research and environment teams * Integrate RL environments into the training stack ...

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

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

As a Principal Machine Learning Engineer, you will work at the intersection of applied ML and platform engineering-collaborating closely with Research Scientists, Data Scientists, and ML Platform ...

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

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

Lead Machine Learning Engineer

OR · On-site +1

$102K - $134K/yr

We are seeking Machine Learning Leaders in the Autonomous Vehicle domain. As part of our team, you ... PhD and/or published research in the described specialty domains. * Familiar with common post ...

... and senior leaders is an important part of the Solutions Architect role, and will give you ... Deep Neural Network or Machine Learning R&D with energy data. Your base salary will be determined ...

Applied Machine Learning Research at Netflix drives various aspects of our business, including personalization, recommendations, search, content understanding, messaging, targeting, new member ...

Applied Scientist

OR · On-site +1

The team conducts machine learning research, evaluates model performance, and partners closely with engineering teams to translate promising ideas into scalable model improvements. As an Applied ...

Showing results 41-60

Senior Machine Learning Researcher information

What does a senior machine learning researcher do?

A Senior Machine Learning Researcher leads the development and application of advanced machine learning models to solve complex problems. They are responsible for designing experiments, analyzing large datasets, publishing research findings, and collaborating with engineering teams to implement solutions. Additionally, they mentor junior researchers, stay updated with the latest advancements in AI, and often contribute to setting the research agenda for their organization.

What are the key skills and qualifications needed to thrive as a senior machine learning researcher, and why are they important?

To thrive as a Senior Machine Learning Researcher, you need advanced knowledge in machine learning algorithms, statistical analysis, programming (typically in Python), and a relevant advanced degree such as a PhD or Master's in computer science or a related field. Experience with frameworks like TensorFlow or PyTorch, as well as familiarity with cloud computing platforms and research publication, is often required. Strong problem-solving, collaboration, and communication skills help you work effectively with cross-functional teams and present complex ideas clearly. These skills and qualities are essential for driving innovation, developing robust models, and translating research into practical, impactful solutions.

What opportunities for collaboration typically exist for senior machine learning researchers within a company?

Senior Machine Learning Researchers frequently collaborate with cross-functional teams, including data engineers, software developers, and domain experts. This collaboration ensures that research insights are effectively translated into scalable solutions and integrated into products or services. Researchers often participate in brainstorming sessions, code reviews, and joint publications, fostering a culture of innovation and shared knowledge. These interactions not only drive the success of projects but also provide valuable learning experiences and networking opportunities.

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

AspectSenior Machine Learning ResearcherData Scientist
CredentialsAdvanced degrees in CS, ML, or related fieldsDegree in CS, statistics, or related fields; certifications optional
Work EnvironmentResearch labs, R&D teams, academiaBusiness analytics, product teams, startups
Industry UsageResearch-focused roles in tech, academia, R&DData analysis, business insights, product development
Search & Comparison IntentUnderstanding research vs applied roles in MLExploring data analysis careers and skills

While both roles involve working with data and machine learning, a Senior Machine Learning Researcher primarily focuses on developing new algorithms and advancing ML theory in research settings. In contrast, a Data Scientist applies existing models to analyze data, generate insights, and support business decisions. The roles differ mainly in their focus—research innovation versus practical application—though they share overlapping skills and credentials.

What are popular job titles related to Senior Machine Learning Researcher jobs in Oregon?

For Senior Machine Learning Researcher jobs in Oregon, the most frequently searched job titles are:

What cities in Oregon are hiring for Senior Machine Learning Researcher jobs?

Cities in Oregon with the most Senior Machine Learning Researcher job openings:

Infographic showing various Senior Machine Learning Researcher job openings in Oregon as of June 2026, with employment types broken down into 2% As Needed, 49% Full Time, 47% Part Time, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution.

Machine Learning Engineer

Bespoke Labs

Gresham, OR • On-site

Full-time

Re-posted 17 days ago


Job description

  • Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch

  • Build and maintain the infrastructure around RL training: rollout collection, data curation, reward model serving, and experiment orchestration

  • Run and scale training experiments on cloud or HPC (AWS, GCP, SLURM, Ray), and debug throughput, stability, and convergence issues

  • Build evaluation harnesses and benchmark infrastructure, with held-out sets and contamination controls, so results are trustworthy

  • Read eval signal and training curves to determine whether a change actually helped, and feed findings back to the research and environment teams

  • Integrate RL environments into the training stack, working with environment authors on interfaces, reward plumbing, and agent loop mechanics

  • Implement methods from recent ML papers quickly and turn them into production-grade systems