Software Engineering Manager, Systems Machine Learning

Description

Meta is looking for a Software Engineering Manager to lead a systems machine learning engineering team building the infrastructure, training pipelines, and serving systems that power AI-driven products at massive scale. In this role, you will drive the technical direction and execution of ML systems work spanning model training efficiency, distributed inference, and ML platform reliability. You will partner closely with applied ML researchers, product engineers, and data scientists to accelerate the development and deployment of machine learning models that serve billions of users across Meta's family of apps and platforms.

Responsibilities

Lead and manage a team of systems ML engineers delivering on multiple concurrent projects spanning training infrastructure, model serving, and ML platform reliability Own the team's AI adoption strategy, identifying opportunities to integrate AI-assisted workflows and automation to expand engineering scope and efficiency Drive roadmap creation for ML systems investments, balancing model training throughput, inference latency, and infrastructure cost across large-scale distributed environments Contribute hands-on to technical decisions in systems ML, including architecture reviews of distributed training pipelines, serving frameworks, and hardware-software co-design Collaborate across product engineering, applied research, and data science teams to ensure ML systems investments translate directly into measurable product impact Proactively identify and resolve bottlenecks in ML infrastructure, including on-call load, technical debt in training and serving stacks, and opportunities for AI-driven automation Set clear goals and expectations for engineers, provide timely and actionable feedback, and actively develop team members' technical capabilities including AI-augmented engineering workflows Communicate ML systems strategy and team goals effectively to engineering leadership and cross-functional partners, sharing context across disciplines to enable engineers to contribute beyond traditional boundaries Lead AI workflow learnings and best practices across the broader engineering organization, contributing to org-wide improvements in ML systems productivity Recruit, onboard, and retain systems ML engineers, building a healthy and high-performing team as measured by execution quality, retention, and team feedback

Qualifications

4+ years of experience managing software engineering teams, including teams working on machine learning infrastructure, distributed systems, or ML platform engineering Experience driving technical roadmaps for ML systems domains such as distributed training, model serving, inference optimization, or ML pipeline development Experience completing coding tasks and contributing to technical decisions in systems ML or distributed computing contexts, including using AI tools to accelerate engineering work Experience partnering cross-functionally with applied ML researchers, product engineers, and data scientists to deliver ML systems that achieve measurable product outcomes Experience identifying and resolving engineering efficiency issues such as infrastructure reliability, on-call burden, and technical debt in large-scale ML systems Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Experience managing teams that operate ML infrastructure supporting production systems at internet scale Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Track record of leading AI workflow adoption within an engineering team, including identifying automation opportunities that meaningfully expand team scope Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Familiarity with ML compilers, model optimization techniques such as quantization or distillation, or accelerator programming for training and inference Experience with large-scale distributed training frameworks, inference serving systems, or hardware-software co-design for ML workloads

Compensation: $184,000/year to $257,000/year + bonus + equity + benefits