Meta
Software Engineer, Systems ML - Compilers / Kernels
$154K–$217K
Bellevue, WA +3 · Full-time · Posted Oct 8, 2026
- Location
- Bellevue, WA
Menlo Park, CA
Santa Clara, CA
New York, NY - Experience
- 6+ years of software engineering experience with a focus on systems software, ML infrastructure, or distributed computing.
- Education
- Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
About the job
Designs, builds, and optimizes systems software for large-scale machine-learning training and inference across Meta’s distributed infrastructure and accelerated hardware.
What you'll do
- Develop and maintain scalable, reliable ML runtime and distributed systems, including scheduling and resource management.
- Profile and optimize ML pipelines and software for compute, memory, and I/O performance.
- Collaborate with researchers and hardware teams to map deep-learning models efficiently to accelerated hardware.
What they ask for
- Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience.
- 6+ years of software engineering experience focused on systems software, ML infrastructure, or distributed computing.
- Experience designing and optimizing large-scale ML training or inference systems, and analyzing performance.
- Experience building production-grade distributed systems with observability, monitoring, and incident response practices.
Nice to have
- Experience with ML frameworks, compiler technologies or graph-level optimizations, and GPU or AI accelerator programming models.
- Experience with responsible AI practices and integrating AI tools to improve workflows.
Pay
$154,003/year to $217,000/year USD base salary
Tools
PyTorch, TensorFlow, JAX, MLIR, LLVM, TVM, XLA, CUDA
AI focus
Build and optimize infrastructure for large-scale ML training and inference; support efficient execution of deep-learning models on accelerated hardware. The posting also asks for ongoing AI skill development, use of AI tools to improve workflows, and responsible AI practices.
Model-written summary · the original posting is authoritative