软件工程师，AI内核与性能优化 — MTIA软件

Meta（Meta Platforms）岗位：Software Engineer, AI Kernels & Performance Optimization — MTIA Software Responsibilities

Design, implement, and optimize high-performance compute and communication kernels for MTIA accelerators, taking ownership from architectural analysis through production deployment

Profile and root-cause performance across the full stack — instruction scheduling, memory hierarchy and DMA behavior, on-chip interconnect, multi-device collectives — and drive the fixes to the right layer, whether that is the kernel, the compiler, the runtime, or the hardware

Build and extend kernel authoring frameworks, templates, and libraries so that other engineers can reach high performance without deep architectural expertise;

Deliver and maintain broad kernel coverage for PyTorch operators across recommendation, ranking, and generative AI workloads, in both eager and compiled execution paths

Partner with silicon architecture and design teams on hardware/software co-design: quantify the value of proposed features with real kernels, characterize rooflines pre-silicon, and advocate for the changes the software stack actually needs

Work with compiler, runtime, framework, and product-facing teams to land end-to-end wins on production models rather than isolated microbenchmark improvements

Investigate numerics and precision trade-offs, and design software mitigations that recover performance or accuracy lost to hardware limitations

Set technical direction for a kernel domain, write the design documents that align cross-functional partners, and mentor engineers on performance methodology and accelerator programming

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience

Bachelor's degree in Computer Science, Computer Engineering, a related technical field, or equivalent practical experience

6+ years of professional experience in high-performance computing, accelerator kernel development, compiler backends, or systems performance engineering

Proficiency in C++ and Python, including low-level systems programming, templates and generic programming, and comfort reading and writing performance-critical code

Demonstrated experience writing and optimizing kernels for a parallel architecture — GPU (CUDA, ROCm/HIP, SYCL/OpenCL), TPU or other AI ASICs, or SIMD/vector CPU targets

Working knowledge of computer architecture as it applies to performance: memory hierarchies and bandwidth, latency hiding, occupancy and scheduling, vectorization, and synchronization

A rigorous, measurement-driven approach to performance: the ability to build a roofline or analytical model, profile against it, and explain the residual gap

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

Deep familiarity with transformer and attention kernel design: FlashAttention-class algorithms, KV-cache management, paged and chunked attention, linear and state-space attention variants, MoE routing and expert dispatch

8+ years of experience in accelerator software, HPC, or ML systems performance (or equivalent with an advanced degree)

Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)

Experience with compiler and codegen technologies relevant to kernels: MLIR, LLVM, TVM, XLA, Halide, or polyhedral scheduling

Experience with low-precision numerics and quantization — FP8/E4M3/E5M2, MX and other block-scaled formats, INT8/INT4 — including error analysis and calibration

Experience with distributed execution and collective communication (NCCL/RCCL-class primitives, tensor and expert parallelism, overlapping communication with compute)

Experience building or contributing to high-performance kernel libraries or frameworks — CUTLASS, cuBLAS, cuDNN, CUTE, Triton, Helion, ThunderKittens, oneDNN, Composable Kernel, or comparable internal equivalents

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

Experience with pre-silicon software development — architectural simulators, FPGA emulation, performance modeling — and with hardware/software co-design cycles

Familiarity with ML framework internals: PyTorch dispatch and eager execution, torch.compile / Inductor, custom operator integration, and inference serving stacks such as vLLM or SGLang

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Bachelor's degree in Computer Science, Computer Engineering, a related technical field, or equivalent practical experience.

6+ years of professional experience in high-performance computing, accelerator kernel development, compiler backends, or systems performance engineering.