Software Engineer, Systems ML (Technical Leadership)

Experteer Overview As a Principal Software Engineer in Systems ML Engineering, you shape the architectural foundations of large-scale ML infrastructure. You define multi-year roadmaps and drive cross-team execution to deliver training, inference, and compiler optimization capabilities. You’ll tackle the toughest cross-system ML infrastructure challenges and enable AI-native workflows that amplify engineering impact. You work closely with research, hardware, and product teams to translate advances into production, with a strong emphasis on reliability and performance. This role offers an opportunity to influence technical strategy at scale and help Meta stay at the forefront of AI-driven infrastructure. Compensation / Benefits * Identify and solve complex cross-system ML infrastructure challenges across training, inference, compiler optimization, and hardware-software co-design * Define extensible architectural standards and foundations ensuring consistency and reliability across multiple orgs * Own multi-year technical roadmap for ML systems infrastructure balancing short-term delivery and long-term platform health * Leverage AI-native tooling to reduce engineering toil and accelerate cross-disciplinary work * Drive performance improvements for large-scale ML training/inference systems across subsystems and abstraction layers * Establish invariants, correctness proofs, and systemic reliability practices to prevent failures * Collaborate with research, hardware, and product teams to translate ML advances into production gains * Assess emerging AI and computing technologies and influence organizational strategy * Mentor engineers, lead programs, and foster a culture of rigor and craftsmanship in ML systems Tasks * Bachelor in Computer Science (or related field) and 12+ years in software engineering with ML systems specialization * Experience architecting and delivering large-scale ML training or inference infrastructure with measurable cross-team impact * Proven track record leading multi-year cross-functional initiatives with metrics, dependencies, and cross-org execution * Proficient in high-performance ML systems infrastructure using C++, Python, or CUDA * Experience influencing technical direction across multiple teams via proposals, design reviews, and stakeholder alignment * Familiarity with ML compiler stacks or hardware-software co-design for ML accelerators Key requirements *

_ orgs * Own multi-year technical roadmap for ML systems infrastructure balancing short-term delivery and long-term platform health * Leverage AI-native tooling to reduce engineering toil and accelerate cross-disciplinary work * Drive performance improvements for large-scale ML training/inference systems across subsystems and abstraction layers * Establish invariants, correctness proofs, and systemic reliability practices to prevent failures * Collaborate with research, hardware, and product teams to translate ML advances into production gains * Assess emerging AI and computing technologies and influence organizational strategy * Mentor engineers, lead programs, and foster a culture of rigor and craftsmanship in ML systems Tasks * Bachelor in Computer Science (or related field) and 12+ years in software engineering with ML systems specialization * Experience architecting and delivering large-scale ML training or inference infrastructure with measurable cross-team impact * Proven track aaa and leading multi-year cross-functional initiatives with metrics, dependencies, and cross-org execution * Proficient in high-performance ML systems infrastructure using C++, Python, or CUDA * Experience influencing technical direction across multiple teams via proposals, design reviews, and stakeholder alignment * Familiarity with ML compiler stacks or hardware-software co-design for ML accelerators Key requirements *