系统机器学习编译器软件工程师

Meta（Meta Platforms）岗位：Software Engineer, Systems ML - Compilers / Kernels 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

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

Experience with ML frameworks such as PyTorch, TensorFlow, or JAX, including framework internals, operator development, or runtime optimization

Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies

Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)

Experience with compiler technologies or graph-level optimizations for deep learning, 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 (e.g., risk assessment, bias mitigation, quality and accuracy reviews)

Experience applying responsible AI practices in systems design, including bias mitigation, model quality evaluation, or AI risk assessment

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Individual compensation is determined by skills, qualifications, experience, and location.

Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable.

In addition to base compensation, Meta offers benefits.

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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.