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Software Engineer, LLVM Compiler - MTIA

Meta· Engineering
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Menlo Park, CA$184k - $257kFull-timePosted 4d ago

About the role

Software Engineer role focusing on GenAI inference for MTIA, spanning serving frameworks, distributed inference, graph-mode execution and compilation, and core PyTorch integration within Meta’s MTIA AI accelerator stack.

Responsibilities

  • Serve as technical owner and domain expert for a key GenAI inference framework area: serving runtime, distributed inference, graph-mode execution and compilation, or core PyTorch integration
  • Lead ambiguous, multi-quarter technical programs end to end: technical design, execution, test strategy and CI, rollout, and production hardening across teams and org boundaries
  • Design, implement, and ship features in generative AI inference frameworks and the MTIA integration layers beneath them, from prototype through production deployment
  • Own performance for production inference workloads end to end: profile across frameworks, runtime, compiler, and kernel boundaries, identify where time actually goes, and drive the fix to the correct layer rather than the convenient one
  • Build and extend the distributed inference substrate: hierarchical KV caching, cross-host transfer paths, disaggregated prefetch and decode, and parallelism strategies for long-context and mixture-of-experts serving
  • Optimize the runtime and execution path: graph capture and replay, host-side latency, memory allocation and placement, and throughput under real traffic conditions
  • Enable frontier models on MTIA and validate accuracy against GPU baselines, closing correctness gaps and distinguishing real regressions from stale references
  • Own the quality bar for your area, ensuring accuracy, stability, latency, and throughput, and test coverage with regression detection that keeps a win from quietly eroding
  • Partner with kernel, compiler, and silicon teams on hardware/software co-design, producing framework-level evidence that shapes the future silicon while the design can still change
  • Provide technical leadership: mentor other engineers, raise the design quality through reviews, and communicate decisions clearly through design documents and cross-team reviews

Qualifications

  • Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
  • 8+ years of professional experience in systems software, ML infrastructure, performance engineering, or compiler/framework development
  • 5+ years of hands-on experience with generative AI inference or training frameworks, or production model-serving systems
  • Proficiency in Python, C++, or Rust, including low-level systems code and performance-critical paths
  • Experience with GenAI inference optimization: prefill and decode optimization, KV-cache management and compression, batching and scheduling, and reasoning about latency and throughput tradeoffs
  • Hands-on experience with deep learning framework internals beyond model authoring — operator registration and dispatch, eager execution, autograd, or graph capture and compilation paths
  • Experience with runtime-level optimization: graph-mode execution, host-side latency reduction, memory allocation and placement
  • Demonstrated experience leading technical design and end-to-end delivery of frameworks or infrastructure projects, including test strategies and CI, across team boundaries
  • Experience working across multiple layers of a system stack (framework, compiler, runtime, hardware) and root-causing issues across component boundaries
  • Experience debugging numerical accuracy issues and defending accuracy and stability bars, not just performance

Skills

Required:GenAI inference optimizationDeep learning framework internalsRuntime optimization

Benefits

  • benefits information available in posting (access via provided link)
Last updated Oct 23, 2026. Interested? Apply on the company site.

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