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Software Engineer, Systems ML - Compilers / Kernels

Meta· Engineering
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Hybrid (Bellevue, WA)$154k - $217kFull-timePosted 1w ago

About the role

Software Engineer role focusing on systems-level ML infrastructure for large-scale AI workloads. Design, build, and optimize software systems enabling efficient training and inference of large-scale deep learning models across Meta’s production infrastructure, at the intersection of ML frameworks, distributed systems, and hardware acceleration.

Responsibilities

  • Design and implement scalable systems-level software components that support large-scale ML training and inference workloads across distributed compute infrastructure
  • Identify and resolve performance bottlenecks in ML pipelines through profiling, instrumentation, and targeted optimization of compute, memory, and I/O utilization
  • Develop and maintain high-quality, well-tested software for ML runtime systems, including scheduling, resource management, and execution frameworks
  • Collaborate with AI researchers and hardware teams to co-design software solutions that efficiently map deep learning model architectures onto accelerated hardware platforms
  • Own the technical design of systems components, evaluating trade-offs across correctness, performance, reliability, and maintainability
  • Establish and monitor service-level objectives and performance metrics for ML infrastructure systems, building dashboards and alerting to reduce mean time to mitigation during incidents
  • Drive adoption of engineering best practices including automated testing, staged rollout strategies using feature flagging and A/B testing frameworks, and comprehensive documentation
  • Mentor other engineers on the team by identifying growth areas, sharing technical expertise, and contributing to code and design reviews
  • Partner with cross-functional stakeholders including product, data science, and operations teams to translate ML research requirements into production-ready systems
  • Proactively identify opportunities to improve developer tooling, automation, and engineering efficiency across the ML systems organization

Qualifications

  • Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
  • 6+ years of experience in software engineering with a focus on systems software, ML infrastructure, or distributed computing
  • Experience designing and optimizing software systems for large-scale machine learning training or inference workloads
  • Experience with performance analysis and optimization techniques including profiling, bottleneck identification, and low-level systems tuning
  • Experience building reliable, production-grade distributed systems with well-defined observability, monitoring, and incident response practices
  • Experience communicating technical decisions through design documents, architectural proposals, or cross-functional stakeholder alignment

Skills

Required:Bachelor's degree equivalent experience
Last updated Oct 23, 2026. Interested? Apply on the company site.

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