Software Engineering Manager, Recommendation Systems

Meta•Menlo Park, CA

2d•$184,000 - $257,000

About The Position

Meta is seeking a Software Engineering Manager to lead a team building and advancing recommendation systems that power personalized experiences across Meta’s family of apps. The role drives technical strategy and execution for ranking, retrieval, and recommendation infrastructure, partnering with product, data science, and infrastructure teams to improve relevance, engagement, and user satisfaction at scale. The manager guides the team’s roadmap and technical decisions, contributes hands-on to system architecture and recommendation infrastructure, and supports engineers’ development through clear goals, feedback, and mentorship. The role also oversees staged rollouts and experimentation, communicates strategy and progress to stakeholders, improves engineering efficiency, and contributes to recruiting and the broader recommendation systems engineering community.

Requirements

At least 4 years of software engineering experience working on recommendation systems, ranking, or related machine learning infrastructure.
At least 2 years of experience managing software engineering teams, including setting goals, providing feedback, and driving execution.
Hands-on contribution to technical decisions in recommendation or ranking systems, including architecture, feature pipelines, or model serving.
Experience driving cross-functional collaboration with product, data science, and infrastructure partners to deliver measurable outcomes.
Experience identifying and resolving engineering efficiency issues such as technical debt, on-call burden, or system scalability constraints.

Nice To Haves

Familiarity with distributed training, model serving infrastructure, or real-time feature computation for recommendation systems.
Experience using experimentation frameworks to measure and iterate on recommendation quality and engagement metrics.
Experience leading teams that have shipped large-scale recommendation or personalization systems from inception to production impact.
Track record of developing engineers’ technical skills and expanding team scope through delegation and mentorship.

Responsibilities

Lead engineers building and optimizing recommendation and ranking systems across retrieval, scoring, and re-ranking stages.
Create the roadmap for recommendation infrastructure, balancing near-term delivery with long-term technical investments.
Contribute hands-on to technical decisions, including system architecture, model serving pipelines, and feature engineering frameworks.
Partner with data science, product, and infrastructure teams to align system development with business and product goals.
Identify and resolve bottlenecks in recommendation quality, latency, and engineering efficiency, including on-call load and technical debt.
Set clear goals and expectations for team members, provide timely and actionable feedback, and develop engineers’ technical capabilities.
Manage staged rollouts and experimentation frameworks to evaluate recommendation changes and measure engagement impact.
Communicate team strategy, technical decisions, and progress to engineering peers, product partners, and leadership.
Identify opportunities for automation and tooling improvements that expand the team’s scope and efficiency.
Participate in recruiting, onboarding, and organization-level contributions to strengthen the recommendation systems engineering community.