Distinguished Software Engineer, Recommendation Systems (AI/ML)

Meta is seeking a Distinguished Software Engineer with deep machine learning expertise to drive transformative advances in Meta's AI-powered products. In this role, you will operate 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. You will identify and solve the hardest user problems, and leverage AI-native approaches to unlock step-change improvements in Meta's recommendation products.

Responsibilities

• The role: Top applied AI/ML expert in a Recommendation Product
• 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 as a force multiplier to accelerate AI/ML advancement in the product 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 operational metrics 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 applied AI/ML, debugging complex product problems, and building production-grade AI systems, establishing yourself as a sought-after technical coach and technical leader

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• 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 both technical and non-technical audiences, including executive leadership Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, 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 (e.g., prompt/context engineering, harness engineering, agent orchestration) and staying current with emerging AI technologies