Software Engineer, Systems ML

**Summary:** Meta is seeking a principal-level Software Engineer to drive technical strategy and execution across our Systems ML Engineering organization. In this role, you will define the architectural foundations that power large-scale machine learning infrastructure, spanning training systems, inference pipelines, ML compilers, high-performance computing frameworks, and on-device optimization. You will identify and solve the hardest cross-system ML infrastructure challenges, shape multi-year technical roadmaps, and amplify the impact of engineering teams through AI-native workflows and deep systems expertise. This is a role for engineers who identify problems others miss and drive them to resolution at organizational scale. **Required Skills:** Software Engineer, Systems ML Responsibilities: 1. Identify and solve the most complex cross-system ML infrastructure challenges spanning training, inference, compiler optimization, and hardware-software co-design, including problems that have resisted prior solution attempts 2. Define extensible architectural standards and technical foundations for ML systems that enable consistency and reliability across multiple engineering organizations 3. Develop and own the multi-year technical roadmap for ML systems infrastructure, balancing short-term delivery with long-term platform health and competitive positioning 4. Leverage AI-native tooling and workflows as a force multiplier to eliminate entire categories of engineering toil and accelerate cross-disciplinary work across the ML systems stack 5. Drive performance improvements across large-scale ML training and inference systems by identifying bottlenecks that span multiple subsystems, ownership boundaries, and abstraction layers 6. Establish invariants, correctness proofs, and systemic reliability practices that prevent whole classes of failures across ML infrastructure pipelines 7. Partner with research, hardware, and product engineering teams to translate theoretical ML systems advances into production infrastructure that delivers measurable efficiency and capability gains 8. Assess emerging AI and computing technologies, evaluate competitive ML infrastructure trends, and influence organizational strategy to ensure technical competitiveness 9. Mentor engineers across the organization by providing customized coaching, leading engineering programs, and establishing a culture of thoroughness and high craft in ML systems development 10. Communicate complex ML systems architecture and strategy clearly to technical and non-technical audiences, producing reference-quality design documents and roadmap artifacts **Minimum Qualifications:** Minimum Qualifications: 11. 12+ years of experience in software engineering with deep specialization in one or more ML systems domains including AI infrastructure, ML compilers, high-performance computing, GPU architecture, ML frameworks, or on-device optimization 12. Experience architecting and delivering large-scale ML training or inference infrastructure that has had measurable impact across multiple engineering organizations 13. Experience leading multi-year cross-functional technical initiatives, including defining metrics, managing dependencies, and driving execution across organizational boundaries 14. Experience developing high-performance ML systems infrastructure in C++, Python, or CUDA, including work at the intersection of hardware and software 15. Experience influencing technical direction and engineering practices across multiple teams through written proposals, design reviews, and stakeholder alignment **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. Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies 18. Experience contributing to industry-wide ML systems efforts through publications, open-source projects, or standards bodies 19. Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) 20. Track record of applying AI tools and automation to redesign engineering workflows, with demonstrated efficiency or quality improvements at organizational scale 21. Experience with ML compiler stacks such as MLIR, XLA, or TVM, or with hardware-software co-design for custom ML accelerators 22. Experience defining and operationalizing reliability, performance, and correctness standards for distributed ML training or large-scale inference systems **Public Compensation:** $219,000/year to $301,000/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. 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