内推 可 relocate 悉尼 远程 Senior AI Researcher (KV Cache Optimization)

内推 可 relocate 悉尼 远程 Senior AI Researcher (KV Cache Optimization)

团队直招 简历直达 HC 1 个 Full Time 岗位

简介

base 悉尼的一家 startup 专注于云平台的 cost deduction 如 CPU/内存的调度优化 同时也正在扩展 GPU 领域 需要懂 CUDA/KV cache/LLM optimization/调度优化的大牛

办公地点选择

• 远程 约 2-3h 时差
• relocate 到悉尼 Hybrid: 3 天办公室+2 天 WFH,可协助办理签证

Responsibilities:

• inference & Compute Optimization: Design and implement highly optimized inference pipelines and computational kernels to accelerate LLM and neural network workloads, leveraging low-level techniques such as SIMD vectorization, cache-aware memory access patterns, and hardware-specific tuning.
• Neural Network Compression & Model Optimization: Research and implement pruning, quantization, and other compression techniques to reduce model size and accelerate inference while preserving accuracy. Apply both in-training and post-training optimization methods across LLM and vision model workloads.
• Profiling & Observability: Build and utilize advanced profiling tools to identify bottlenecks across the inference and training stack-from memory bandwidth and cache utilization to CPU-side data preprocessing stalls and end-to-end pipeline throughput.
• Evaluation & Benchmarking: Design and maintain rigorous evaluation and benchmarking frameworks for systematic model comparison across optimization configurations. Develop automated pipelines (e.g., LLM-as-a-judge) to measure the impact of optimization techniques on model quality and performance.
• Mentorship: Act as a technical lead for engineers and researchers, fostering a culture of high-performance code, rigorous benchmarking, and research-to-production excellence. Drive team growth, technical interviews, and cross-functional collaboration.

Required Qualifications

• Deep Systems Expertise: 8+ years of experience in high-performance computing, AI systems, or low-level software optimization. Deep familiarity with performance-critical development including CPU/GPU architecture, memory hierarchies, SIMD/vectorization, and profiling-driven tuning.
• LLM & NN Optimization Track Record: Proven experience optimizing neural networks and LLMs through techniques such as pruning, quantization, and inference acceleration, with a demonstrated path from research to production deployment.
• Communication: Ability to translate complex systems-level constraints and optimization trade-offs into actionable research directions for modeling and engineering teams.
• Experience building evaluation frameworks, ML observability, or developer tools that help researchers understand and compare model performance across optimization configurations.
• A history of working on neural network compression, inference acceleration, or applied AI research problems that required bridging algorithmic research with high-performance implementation.
• Patent authorship or published research in AI/ML optimization.
• Experience with C/C++ inference engines, x86 intrinsics, or similar low-level performance work is a strong plus.

备注

• 以上条件不需全部满足 符合部份条件有兴趣的欢迎您来
• 年龄不限制 但是要 senior 以上的
• 不是我面试 只是内推 全英面试 需要准备好英文简历
• 因薪资范围公司 JD 没提到 可以参考 其中描述基本还是准确的 自己按照预期来提 没有上家流水检查和涨幅限制说法
• 可谈 Employee Stock Ownership Plan (ESOP) package