Software Engineer, Machine Learning

Meta•Menlo Park, CA

3d•$347,000 - $403,000

About The Position

Meta is seeking a distinguished Software Engineer with deep machine learning expertise to advance AI-powered products and platforms. The role sits at the intersection of foundational ML research and large-scale production systems, shaping machine learning infrastructure, modeling, and applied AI across the organization. The engineer will address complex ML systems challenges, establish architectural standards, and use AI-native approaches to improve how intelligent systems are built and deployed at global scale.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, a 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 involving 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 technical and non-technical audiences, including executive leadership.

Nice To Haves

Ability to integrate AI tools to optimize or redesign workflows and drive measurable impact, such as efficiency gains or quality improvements.
Experience building AI-native developer tooling or automation that measurably accelerates ML experimentation and production deployment cycles.
Ongoing AI skill development, such as prompt or context engineering and agent orchestration, and staying current with emerging AI technologies.
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, such as risk assessment, bias mitigation, and quality and accuracy reviews.

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 complex ML systems challenges across product areas, including issues involving model quality, training efficiency, serving latency, and data integrity.
Develop extensible ML frameworks, modeling standards, and engineering practices that improve consistency and velocity across engineering organizations.
Lead cross-functional technical strategy for ML initiatives, aligning research, infrastructure, and product teams around multi-year roadmaps.
Apply AI-native workflows and tooling to accelerate model development, automate evaluation pipelines, and expand engineering capabilities.
Define metrics and data-driven decision-making principles for long-term ML projects, linking model performance to organization-level business outcomes.
Identify systemic reliability, privacy, and integrity risks in ML systems and build technical safeguards in partnership with compliance and policy teams.
Mentor engineers on ML systems design, complex model debugging, and production-grade AI systems.
Improve the performance of large-scale ML systems by identifying bottlenecks across training, data loading, model serving, and hardware utilization, and leading cross-organizational efforts to address them.
Influence the ML engineering community through technical publications, design frameworks, and cross-industry engagement.