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Temporary Meta Machine Learning Jobs in Seattle, WA

Meta

Engineering Manager, Machine Learning

Bellevue, WA · On-site

$219K/yr

Meta is seeking an Engineering Manager to lead machine learning engineering teams building state-of-the-art recommendation systems at scale. In this role, you will manage teams of ML engineers and ...

Meta

Software Engineer, Machine Learning

Seattle, WA · On-site

$183K/yr

Meta is seeking talented experienced engineers to join our teams in building cutting-edge products ... Software Engineer, Machine Learning Responsibilities: * Collaborate with cross-functional teams ...

Meta

Software Engineer, Machine Learning

Bellevue, WA · On-site

$183K/yr

Meta is seeking talented experienced engineers to join our teams in building cutting-edge products ... Software Engineer, Machine Learning Responsibilities: * Collaborate with cross-functional teams ...

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How much do temporary meta machine learning jobs pay per hour?

As of Sep 6, 2026, the average hourly pay for temporary meta machine learning in Seattle, WA is $25.97, according to ZipRecruiter salary data. Most workers in this role earn between $22.45 and $28.99 per hour, depending on experience, location, and employer.

What is a temporary Meta machine learning job?

Temporary Meta Machine Learning jobs are short-term positions at Meta (formerly Facebook) that focus on developing, deploying, or researching machine learning models and technologies. These roles may support ongoing projects, fill gaps during employee leave, or address spikes in workload. Responsibilities can include data preprocessing, model training, evaluation, and collaborating with cross-functional teams. Temporary roles often give candidates exposure to Meta's cutting-edge AI tools and processes, and may sometimes lead to permanent opportunities.

What are the key skills and qualifications needed to thrive as a temporary Meta machine learning engineer?

To thrive as a Temporary Meta Machine Learning Engineer, you need a strong background in computer science, statistics, and machine learning, typically with experience in Python and relevant ML frameworks. Familiarity with tools such as TensorFlow, PyTorch, cloud platforms, and version control systems is often required, along with a proven ability to rapidly learn new technologies. Strong problem-solving skills, adaptability, and effective communication are essential for collaborating within dynamic teams and meeting project goals on tight timelines. These skills ensure that you can quickly contribute to impactful ML projects, deliver results efficiently, and integrate well into fast-paced, innovative environments.

What are some common challenges faced by professionals in temporary machine learning roles at Meta, and how can they be addressed?

Professionals in temporary machine learning roles at Meta often encounter challenges such as quickly acclimating to complex codebases, integrating with established teams, and delivering impactful results within a limited timeframe. Success in these roles typically requires strong technical skills, adaptability, and effective communication. Proactively seeking guidance, leveraging available documentation, and collaborating closely with permanent team members can help overcome these hurdles and maximize contributions during the temporary assignment.

What is the difference between Temporary Meta Machine Learning vs Data Scientist?

AspectTemporary Meta Machine LearningData Scientist
CredentialsTypically requires a background in computer science, statistics, or related fields; certifications in machine learning or data analysis are commonRequires a degree in computer science, statistics, or related fields; certifications like Certified Data Scientist are advantageous
Work EnvironmentProject-based, often contract roles within tech companies, startups, or consulting firmsFull-time or contract roles in various industries including finance, healthcare, and tech
Industry UsagePrimarily in tech, AI, and machine learning-focused companiesWidely used across multiple industries including finance, healthcare, marketing, and tech

Temporary Meta Machine Learning roles focus on short-term projects involving machine learning model development and deployment, often requiring specialized technical skills. Data Scientist roles are broader, encompassing data analysis, statistical modeling, and insights generation across diverse industries. While both roles require strong analytical skills and technical knowledge, Temporary Meta Machine Learning positions are more specialized in AI and machine learning applications.

What are the most commonly searched types of Meta Machine Learning jobs in Seattle, WA?

The most popular types of Meta Machine Learning jobs in Seattle, WA are:

What are popular job titles related to Temporary Meta Machine Learning jobs in Seattle, WA?

For Temporary Meta Machine Learning jobs in Seattle, WA, the most frequently searched job titles are:

What job categories do people searching Temporary Meta Machine Learning jobs in Seattle, WA look for?

The top searched job categories for Temporary Meta Machine Learning jobs in Seattle, WA are:

Temporary Meta Machine Learning jobs near you
Infographic showing various Temporary Meta Machine Learning job openings in Seattle, WA as of June 2026, with employment types broken down into 94% Full Time, 3% Part Time, 2% Temporary, and 1% Contract. Highlights an 99% Physical, and 1% Remote job distribution, with an average salary of $54,020 per year, or $26 per hour.

Engineering Manager, Machine Learning

Meta

Bellevue, WA • On-site

$219K/yr

Full-time

Posted 5 days ago

Key responsibilities

  • Manage multiple teams of ML engineers and technical leaders delivering large-scale recommendation and ranking systems.

  • Drive the technical strategy and roadmap for Meta Recommendation Systems initiatives, influencing model architectures, algorithms, and deployment approaches.

  • Partner with product, data science, and research teams to define recommendation system problem formulations, prioritize experiments, and translate model improvements into product outcomes.

Meta rating

7.8

Company rating: 7.8 out of 10

Based on 45 frontline employees who took The Breakroom Quiz

139th of 247 rated software companies

Job description

Meta is seeking an Engineering Manager to lead machine learning engineering teams building state-of-the-art recommendation systems at scale. In this role, you will manage teams of ML engineers and technical leaders working on Meta Recommendation Systems (MRS) — from data pipelines and model development to training infrastructure and production deployment. You will shape the technical strategy for recommendation and ranking initiatives, drive SOTA model adoption within your teams, and partner closely with product, data science, and research to deliver recommendation systems that have meaningful impact across Meta's products and platforms.
Engineering Manager, Machine Learning Responsibilities:
  • Manage multiple teams of ML engineers and technical leaders delivering large-scale recommendation and ranking systems across model development, training, evaluation, and production serving
  • Drive the technical strategy and roadmap for MRS initiatives, influencing decisions around SOTA model architectures, recommendation algorithms, and deployment approaches
  • Actively engage with technical direction and code quality across teams, staying current with state-of-the-art research in recommendation systems and applying cutting-edge techniques
  • Partner with product, data science, and research to define recommendation system problem formulations, prioritize ranking experiments, and translate model improvements into measurable product outcomes
  • Recruit, develop, and retain ML engineers and engineering leaders with deep expertise in recommendation systems and ranking models
  • Champion adoption of SOTA techniques in recommendation systems, including deep learning approaches, transformer-based models, and multi-objective optimization
  • Proactively identify and resolve execution risks across recommendation system projects, including data quality issues, model performance regressions, training instability, and infrastructure bottlenecks
  • Establish a team culture that values code quality, rigorous experimentation practices, and continuous learning from recommendation systems research
  • Hold leaders accountable for performance, technical depth in ranking and personalization, and cross-functional engagement
  • Represent the team's work and priorities to leadership, communicating recommendation system tradeoffs, SOTA advances, and strategic implications clearly

Minimum Qualifications:
  • 8+ years of experience in software engineering with a focus on machine learning systems, including model development, training pipelines, or ML infrastructure
  • 4+ years of experience managing engineering teams, including experience managing other engineering leaders
  • Experience driving technical strategy and roadmap decisions for ML systems across the full model lifecycle, from data ingestion through production serving
  • Experience partnering cross-functionally with product, data science, and research teams to define ML problem scope and deliver measurable outcomes
  • Experience recruiting, developing, and retaining ML engineering talent and building high-performing teams in ambiguous, high-impact areas

Preferred Qualifications:
  • Hands-on background in ML model development using frameworks such as PyTorch or TensorFlow, with specific experience in recommendation models
  • Experience managing teams working on large-scale recommendation, ranking, or retrieval systems in a production environment
  • Experience with large-scale personalization systems, user modeling, and multi-objective ranking optimization
  • Track record of building recommendation systems that directly influenced product metrics at significant scale
  • Track record of implementing SOTA research papers into production recommendation and ranking systems
  • Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
  • Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
  • Demonstrated ability to evaluate and adopt emerging techniques from top ML conferences (RecSys, KDD, NeurIPS, ICML) into production systems
  • Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
  • Experience with state-of-the-art recommendation system architectures including deep learning, transformer-based models, and neural collaborative filtering

About Meta:
Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.
Meta is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Meta participates in the E-Verify program in certain locations, as required by law. Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment.
Meta is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at accommodations-ext@meta.com.
$219,000/year to $301,000/year + bonus + equity + benefits
Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about benefits at Meta.

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