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Temporary Meta Machine Learning Jobs in Sunnyvale, CA

Meta

Software Engineer, Machine Learning

Sunnyvale, CA

$347K/yr

Meta is seeking a distinguished Software Engineer with deep machine learning expertise to drive transformative advances across Meta's AI-powered products and platforms. In this role, you will operate ...

Meta

Product Manager, Machine Learning

Menlo Park, CA

$173K/yr

Meta Product Managers work with cross-functional teams of engineers, designers, data scientists and ... Product Manager, Machine Learning Responsibilities: * Display strong leadership, organizational and ...

Meta

Machine Learning SoC Architect

Sunnyvale, CA

$212K/yr

Meta is seeking a Machine Learning SoC Architect for its Silicon Engineering organization responsible for building custom silicon solutions that power the infrastructure underpinning Meta's AI and ...

Meta

Machine Learning SoC Architect

Menlo Park, CA

$212K/yr

Meta is seeking a Machine Learning SoC Architect for its Silicon Engineering organization responsible for building custom silicon solutions that power the infrastructure underpinning Meta's AI and ...

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

As of Aug 19, 2026, the average hourly pay for temporary meta machine learning in Sunnyvale, CA is $26.78, according to ZipRecruiter salary data. Most workers in this role earn between $23.12 and $29.90 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 cities near Sunnyvale, CA are hiring for Temporary Meta Machine Learning jobs?

Cities near Sunnyvale, CA with the most Temporary Meta Machine Learning job openings:

Temporary Meta Machine Learning jobs near you
Infographic showing various Temporary Meta Machine Learning job openings in Sunnyvale, CA as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 21% Part Time, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $55,711 per year, or $26.8 per hour.

Software Engineer, Machine Learning

Meta

Sunnyvale, CA

$347K/yr

Full-time

Re-posted 4 days ago

Meta rating

7.8

Company rating: 7.8 out of 10

Based on 45 frontline employees who took The Breakroom Quiz

137th of 245 rated software companies

Job description

Meta is seeking a distinguished Software Engineer with deep machine learning expertise to drive transformative advances across Meta's AI-powered products and platforms. In this role, you will operate at the intersection of foundational ML research and large-scale production systems, shaping the technical direction of machine learning infrastructure, modeling, and applied AI across the organization. You will identify and solve the hardest ML systems challenges, define architectural standards, and leverage AI-native approaches to unlock step-change improvements in how Meta builds and deploys intelligent systems at global scale.
Software Engineer, Machine Learning Responsibilities:
  • Define and own the technical architecture of critical machine learning systems, including model training pipelines, inference infrastructure, and feature engineering platforms, ensuring reliability and scalability across billions of users
  • Identify and solve the most complex ML systems challenges across multiple product areas, including issues that span model quality, training efficiency, serving latency, and data integrity
  • Develop and establish extensible ML frameworks, modeling standards, and engineering practices that drive consistency and velocity across multiple engineering organizations
  • Lead cross-functional technical strategy for machine learning initiatives, aligning research, infrastructure, and product teams around multi-year roadmaps that balance short-term delivery with long-term architectural health
  • Apply AI-native workflows and tooling as a force multiplier to accelerate model development cycles, automate evaluation pipelines, and expand the scope of what engineering teams can deliver
  • Define new metrics and data-driven decision-making principles for long-term ML projects, connecting model performance signals to organization-level business outcomes
  • Proactively identify systemic reliability, privacy, and integrity risks in ML systems and build robust technical safeguards, partnering with compliance and policy teams to ensure responsible AI deployment
  • Mentor engineers across the organization on ML systems design, debugging complex model behavior, and building production-grade AI systems, establishing yourself as a sought-after technical coach and technical leader
  • Drive performance improvements across large-scale ML systems by identifying bottlenecks that span training, data loading, model serving, and hardware utilization, and leading cross-org efforts to resolve them
  • Influence the broader ML engineering community through technical publications, design frameworks, and cross-industry engagement that advances the field

Minimum Qualifications:
  • Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
  • 12+ years of experience designing, building, and deploying large-scale machine learning systems in production environments
  • Experience architecting end-to-end ML platforms spanning data pipelines, distributed training, model evaluation, and low-latency inference serving
  • Experience identifying and resolving complex, cross-system ML failures including issues in model quality, training stability, feature consistency, and serving correctness
  • Experience defining technical strategy and gaining organizational alignment across multiple engineering teams and cross-functional stakeholders
  • Experience communicating complex ML system designs and trade-offs in writing to both technical and non-technical audiences, including executive leadership

Preferred Qualifications:
  • Experience applying ML to multiple product domains such as ranking and recommendation, generative AI, computer vision, or natural language understanding
  • Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
  • Track record of industry-recognized contributions to machine learning systems, such as publications, open-source frameworks, or widely adopted architectural patterns
  • 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
  • Experience building AI-native developer tooling or automation that measurably accelerates ML experimentation and production deployment cycles
  • Experience with large-scale foundation model training, fine-tuning, or inference optimization across distributed hardware clusters

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.
$347,000/year to $403,000/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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