Software Engineer, Systems ML - Compilers / Kernels

Meta•Santa Clara, CA

3d•$154,003 - $217,000

About The Position

Meta is seeking a Software Engineer to work on systems-level machine learning infrastructure supporting large AI workloads. The role involves designing, building, and optimizing software systems for efficient training and inference of large-scale deep learning models across Meta's production infrastructure. Working at the intersection of ML frameworks, distributed systems, and hardware acceleration, the engineer will contribute to the reliability, performance, and scalability of ML platforms powering Meta's AI-driven products.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience.
At least 6 years of software engineering experience focused 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, including profiling, bottleneck identification, and low-level systems tuning.
Experience building reliable, production-grade distributed systems with defined observability, monitoring, and incident response practices.
Experience communicating technical decisions through design documents, architectural proposals, or cross-functional stakeholder alignment.

Nice To Haves

Experience with ML frameworks such as PyTorch, TensorFlow, or JAX, including framework internals, operator development, or runtime optimization.
Ongoing AI skill development, such as prompt or context engineering and agent orchestration, and staying current with emerging AI technologies.
Ability to integrate AI tools to optimize or redesign workflows and drive measurable impact, such as efficiency gains or quality improvements.
Experience with compiler technologies or graph-level deep learning optimizations, such as MLIR, LLVM, TVM, or XLA.
Experience with GPU or AI accelerator programming models such as CUDA, ROCm, or vendor-specific kernel libraries.
Experience adhering to and implementing responsible, ethical AI practices, such as risk assessment, bias mitigation, and quality and accuracy reviews.
Experience applying responsible AI practices in systems design, including bias mitigation, model quality evaluation, or AI risk assessment.

Responsibilities

Design and implement scalable systems-level software components for large-scale ML training and inference across distributed compute infrastructure.
Identify and resolve ML pipeline performance bottlenecks using 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 that efficiently maps deep learning architectures onto accelerated hardware.
Own the technical design of systems components and evaluate trade-offs involving correctness, performance, reliability, and maintainability.
Establish and monitor service-level objectives and performance metrics for ML infrastructure, and build dashboards and alerting to reduce incident mitigation time.
Promote engineering best practices, including automated testing, staged rollouts using feature flags and A/B testing, and comprehensive documentation.
Mentor engineers by identifying growth areas, sharing technical expertise, and contributing to code and design reviews.
Partner with product, data science, and operations stakeholders to translate ML research requirements into production-ready systems.
Identify opportunities to improve developer tooling, automation, and engineering efficiency across the ML systems organization.