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Software Engineer, Systems ML (Technical Leadership)
Meta
Bellevue, USA
Full-time
Senior · 12+ years experience
Onsite
USD 219000-301000 yearly / year
Discovered 1 weeks ago
C++PythonCUDAMLIRXLATVM
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Responsibilities
- 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
- Define extensible architectural standards and technical foundations for ML systems that enable consistency and reliability across multiple engineering organizations
- Develop and own the multi-year technical roadmap for ML systems infrastructure, balancing short-term delivery with long-term platform health and competitive positioning
- 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
- Drive performance improvements across large-scale ML training and inference systems by identifying bottlenecks that span multiple subsystems, ownership boundaries, and abstraction layers
- Establish invariants, correctness proofs, and systemic reliability practices that prevent whole classes of failures across ML infrastructure pipelines
- Partner with research, hardware, and product engineering teams to translate theoretical ML systems advances into production infrastructure that delivers measurable efficiency and capability gains
- Assess emerging AI and computing technologies, evaluate competitive ML infrastructure trends, and influence organizational strategy to ensure technical competitiveness
- 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
- Communicate complex ML systems architecture and strategy clearly to technical and non-technical audiences, producing reference-quality design documents and roadmap artifacts
Minimum Qualifications
- Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
- 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
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