机器学习软件工程师

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

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

Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)

Experience building AI-native developer tooling or automation that measurably accelerates ML experimentation and production deployment cycles

Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies

Track record of industry-recognized contributions to machine learning systems, such as publications, open-source frameworks, or widely adopted architectural patterns

Experience applying ML to multiple product domains such as ranking and recommendation, generative AI, computer vision, or natural language understanding

Experience with large-scale foundation model training, fine-tuning, or inference optimization across distributed hardware clusters

Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)

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.

For those who live in or expect to work from California if hired for this position, please click here for additional information.

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.

Meta is proud to be an Equal Employment Opportunity employer.

We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or other applicable legally protected characteristics.

You may view our Equal Employment Opportunity notice here.

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You may view our Equal Employment Opportunity notice here.

Meta is committed to providing reasonable support (called accommodations) in our recruiting processes for candidates with disabilities, long term conditions, mental health conditions or sincerely held religious beliefs, or who are neurodivergent or require pregnancy-related support.

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12+ years of experience designing, building, and deploying large-scale machine learning systems in production environments.