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Fall Machine Learning Co Op Jobs in Seattle, WA (NOW HIRING)

Weyerhauser Co

ML Engineer

Seattle, WA · On-site

Primary Responsibilities Develop Machine Learning Models Design, build, and optimize machine learning models, including feature engineering, model selection, training, and validation across multiple ...

Slalom Consulting

General Information

Seattle, WA · On-site

$131K - $159K/yr

Apply Machine Learning, Generative AI, and LLM-based techniques to solve real business problems ... to co-create powerful customer experiences, modern ways of working, and meaningful impact. What ...

Chewy

Research Scientist III

Bellevue, WA

$148K - $225K/yr

... machine learning techniques. * Deep dive large supply chain data to explain change, develop insights, and provide recommendations. * Work with collaborators across organizations (with Inbound, S&OP ...

Slalom Consulting

General Information

Seattle, WA · On-site

$106K - $129K/yr

Apply Machine Learning, Generative AI, and LLM-based techniques to solve real business problems ... to co-create powerful customer experiences, modern ways of working, and meaningful impact. What ...

Weyerhauser Co

Data Scientist

Seattle, WA · On-site

We are seeking an experienced Data Scientist to provide technical leadership and passionate about applying machine learning, statistics, experimentation, and optimization techniques to solve complex ...

New

Meta

Research Engineer, Monetization AI

Seattle, WA

$183K/yr

Develop and deploy machine learning pipelines * Collaborate with cross-functional teams to design and optimize ML systems, leveraging expertise in hardware-software co-design, including quantization ...

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All JobsFall Machine Learning Co Op JobsFall Machine Learning Co Op Jobs in Seattle, WA

Fall Machine Learning Co Op information

See Seattle, WA salary details

$29K

$48.5K

$100.1K

How much do fall machine learning co op jobs pay per year?

As of Aug 12, 2026, the average yearly pay for fall machine learning co op in Seattle, WA is $48,461.00, according to ZipRecruiter salary data. Most workers in this role earn between $37,000.00 and $52,300.00 per year, depending on experience, location, and employer.

What is a Fall Machine Learning Co Op?

A Fall Machine Learning Co-Op is a temporary, typically full-time position for students or recent graduates to gain hands-on experience in applying machine learning techniques. These roles usually involve working with data, training models, and optimizing algorithms under the supervision of experienced engineers or researchers. They are offered during the fall semester and can last several months. Companies use these positions to provide practical learning opportunities and assess potential future hires.

What can I expect from the day-to-day experience of a Fall Machine Learning Co Op?

As a Fall Machine Learning Co Op, you'll typically work with a team of data scientists and engineers on real projects that may involve data cleaning, model development, testing, and reporting insights. Your days might include collaborating in meetings, coding, analyzing data, and presenting findings to team members or supervisors. You'll receive mentorship from experienced professionals and have opportunities to participate in code reviews and brainstorming sessions. This structure helps you build technical skills, broaden your professional network, and gain a comprehensive understanding of how machine learning is applied in a business setting.

What are the key skills and qualifications needed to thrive in the Fall Machine Learning Co Op position, and why are they important?

To thrive as a Fall Machine Learning Co Op, you should have a solid background in programming (especially Python), statistics, and machine learning concepts, often supported by coursework or hands-on projects in computer science or related fields. Familiarity with tools like TensorFlow, PyTorch, and data analysis libraries such as pandas and scikit-learn is highly valued, while certifications in AI or data science can be a plus. Strong problem-solving skills, eagerness to learn, effective communication, and teamwork help you stand out in this role. These skills are crucial for contributing to real-world projects, collaborating with technical teams, and gaining valuable experience in a fast-paced, innovation-driven environment.

What are popular job titles related to Fall Machine Learning Co Op jobs in Seattle, WA? For Fall Machine Learning Co Op jobs in Seattle, WA, the most frequently searched job titles are:
What job categories do people searching Fall Machine Learning Co Op jobs in Seattle, WA look for? The top searched job categories for Fall Machine Learning Co Op jobs in Seattle, WA are:
Fall Machine Learning Co Op jobs near you
Infographic showing various Fall Machine Learning Co Op job openings in Seattle, WA as of August 2026, with employment types broken down into 90% Full Time, 5% Part Time, and 5% Contract. Highlights an 100% In-person job distribution, with an average salary of $48,461 per year, or $23.3 per hour.

Manager II, Machine Learning - Conversion Visibility

Pinterest

Seattle, WA • Hybrid

Full-time

Re-posted 11 days ago

Job description

The Conversion Visibility Modeling team enables a performant ads marketplace and helps prove value to advertisers by connecting Pinterest onsite activity with conversions that happen offsite (both digital and physical) in a privacy-preserving way. As a Machine Learning Engineering Manager on this team, you will lead a hybrid team of ML engineers and backend software engineers to build end-to-end identity and conversion visibility solutions across modeling, serving, and data infrastructure, so advertisers retain accurate, privacy-aware performance visibility as signals fragment and degrade. You will set the technical direction for high-impact ML systems that feed ranking, bidding, measurement, and reporting across Pinterest's ads stack.

What you'll do: 

  • Attract, hire, develop, and lead a hybrid team of ML engineers and backend software engineers, fostering strong collaboration across modeling and infrastructure and building an inclusive, high-performing environment where the team can deliver end-to-end solutions. 
  • Lead a team responsible for the strategy, execution, and operational excellence of identity and conversion signal modeling systems (e.g., user match prediction, conversion type/value prediction, probabilistic attribution and deduplication) that improve match precision/recall and downstream conversion quality across web and app surfaces.
  • Partner closely with product managers, data scientists, and tech leads to shape problem definitions, translate business needs into technical strategy, and drive execution toward high-impact outcomes.
  • Collaborate closely with Ads Ranking & Bidding, Measurement Products, and Conversion Ingestion & Attribution teams to define interfaces, SLAs, and success metrics that enable end-to-end identity and conversion visibility systems-including models, data pipelines, and serving surfaces-to integrate cleanly into the broader ads ecosystem.
  • Establish engineering best practices across both ML and backend development, including data quality, feature and data pipelines, model evaluation, experimentation, service reliability, and operational excellence, so the team can build trustworthy ML-powered systems end to end.
  • Use AI to accelerate analysis and iteration on model ideas and architectures, while applying strong judgment, testing, and verification to ensure correctness, reliability, and advertiser trust.

What we're looking for:

  • 7+ years of experience building and deploying large-scale ML systems in production (e.g., ads, measurement, recommendation, ranking, or search).
  • 2+ years of experience as an engineering manager or technical lead.
  • Bachelors Degree in Computer Science, Statistics, or a related technical field, or equivalent experience.
  • Nice to have: Meaningful hands-on experience or strong familiarity with ads conversion attribution, identity matching, ads ranking or ads measurement domains.
  • Proven technical leadership across both ML and software systems, with experience setting direction for multi-quarter roadmaps that span modeling, data pipelines, backend services, and productionization, and aligning stakeholders on priorities, trade-offs, and execution plans.
  • Excellent cross-functional communication and collaboration skills, building strong partnerships with product, data science, infra, and partner ML teams to clarify ambiguous problem spaces, co-create solutions, and drive consensus with senior stakeholders.

Relocation Statement:

  • This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.

In-Office Requirement Statement:

  • We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.
  •  This role will need to be in the office for in-person collaboration 1 day per week and therefore needs to be in a commutable distance from one of the following offices [Seattle or Bay Area].

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