Software Engineer, Machine Learning

Meta•Menlo Park, CA

1d•$347,000 - $403,000

About The Position

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.

Requirements

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

Nice To Haves

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

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