软件工程师，生成式AI框架 - MTIA

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

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

Bachelor's degree in Computer Science, Computer Engineering, a 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

Experience with long-context inference techniques: context and sequence parallelism, ring or streaming attention, hierarchical or offloaded KV cache

Experience with AI accelerator platforms (GPU, TPU, or custom ASICs) and bringing up a new hardware backend in a major machine learning framework

Experience with low-precision numerics and quantization (FP8, block-scaled formats, INT8/INT4), including calibration and accuracy error analysis, debugging tools for distributed systems

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

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

Experience with distributed inference or training at scale: tensor, pipeline, and expert parallelism, collective communication primitives,

Familiarity with the PyTorch compilation stack (TorchDynamo, FX IR, Torch Inductor, dynamic shapes, Triton, AOT I) and custom operator integration

Experience with performance optimization in production environments, with a track record of using profiling and tracing to understand and resolve performance bottlenecks

Experience with LLM inference serving stacks, including continuous batching, paged or chunked attention, speculative decoding, prefix caching, disaggregated prefill and decode, or mixture-of-experts routing and load balancing

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

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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.