Software Engineering Manager, Systems Machine Learning

About the role

**Summary:**

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.

**Required Skills:**

Software Engineering Manager, Systems Machine Learning Responsibilities:

1. Lead and manage a team of systems ML engineers delivering on multiple concurrent projects spanning training infrastructure, model serving, and ML platform reliability

2. Own the team's AI adoption strategy, identifying opportunities to integrate AI-assisted workflows and automation to expand engineering scope and efficiency

3. Drive roadmap creation for ML systems investments, balancing model training throughput, inference latency, and infrastructure cost across large-scale distributed environments

4. Contribute hands-on to technical decisions in systems ML, including architecture reviews of distributed training pipelines, serving frameworks, and hardware-software co-design

5. Collaborate across product engineering, applied research, and data science teams to ensure ML systems investments translate directly into measurable product impact

6. 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

7. Set clear goals and expectations for engineers, provide timely and actionable feedback, and actively develop team members' technical capabilities including AI-augmented engineering workflows

8. 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

9. Lead AI workflow learnings and best practices across the broader engineering organization, contributing to org-wide improvements in ML systems productivity

10. Recruit, onboard, and retain systems ML engineers, building a healthy and high-performing team as measured by execution quality, retention, and team feedback

**Minimum Qualifications:**

Minimum Qualifications:

11. 4+ years of experience managing software engineering teams, including teams working on machine learning infrastructure, distributed systems, or ML platform engineering

12. Experience driving technical roadmaps for ML systems domains such as distributed training, model serving, inference optimization, or ML pipeline development

13. Experience completing coding tasks and contributing to technical decisions in systems ML or distributed computing contexts, including using AI tools to accelerate engineering work

14. Experience partnering cross-functionally with applied ML researchers, product engineers, and data scientists to deliver ML systems that achieve measurable product outcomes

15. Experience identifying and resolving engineering efficiency issues such as infrastructure reliability, on-call burden, and technical debt in large-scale ML systems

**Preferred Qualifications:**

Preferred Qualifications:

16. Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)

17. Experience managing teams that operate ML infrastructure supporting production systems at internet scale

18. Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies

19. Track record of leading AI workflow adoption within an engineering team, including identifying automation opportunities that meaningfully expand team scope

20. Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)

21. Familiarity with ML compilers, model optimization techniques such as quantization or distillation, or accelerator programming for training and inference

22. Experience with large-scale distributed training frameworks, inference serving systems, or hardware-software co-design for ML workloads

**Public Compensation:**

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

**Industry:** Internet

**Equal Opportunity:**

Meta is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Meta participates in the E-Verify program in certain locations, as required by law. Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment.

Meta is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at accommodations-ext@meta.com.

Posting text as published by Meta.

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