Distinguished Software Engineer, Recommendation Systems (AI/ML)

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 advances in Meta's AI-powered products. The role operates at the intersection of foundational ML research and large-scale user products, shaping the technical direction of applied AI, machine learning modeling, and infrastructure across the organization. The engineer will identify and solve difficult user problems and use AI-native approaches to improve Meta's recommendation products. As the top applied AI/ML expert in a Recommendation Product, this engineer will work on the product's hardest applied AI problems, define its long-term architecture, and help engineering teams expand what they can deliver. The role also establishes metrics and decision-making principles for long-term ML projects, addresses systemic risks in ML systems, and mentors engineers across the organization.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience.
8+ years of experience in applied AI/ML with a track record of delivering measurable product outcomes.
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

Experience adhering to and implementing responsible, ethical AI practices, such as risk assessment, bias mitigation, and quality and accuracy reviews.
Experience building AI-native developer tooling or automation that measurably accelerates ML experimentation and production improvement cycles.
Experience applying ML to multiple product domains such as ranking and recommendation, generative AI, computer vision, or natural language processing.
Track record of industry-recognized contributions to machine learning applications, such as publications and open-source projects.
Demonstrated ongoing AI skill development, such as prompt/context engineering, harness engineering, or agent orchestration, and staying current with emerging AI technologies.

Responsibilities

Work on the hardest applied AI problem in the product.
Set a 2–3 year north-star architecture for the product.
Apply AI-native workflows and tooling to accelerate AI/ML advancement and expand engineering teams' capabilities.
Define new metrics and data-driven decision-making principles for long-term ML projects, connecting operational metrics to organization-level business outcomes.
Identify systemic reliability, privacy, and integrity risks in ML systems and build technical safeguards, partnering with compliance and policy teams to support responsible AI deployment.
Mentor engineers across the organization in applied AI/ML, debugging complex product problems, and building production-grade AI systems.