AI 研究员 — 推理优化
AI Researcher — Inference Optimization
职位概述
我们正在寻找一位在推理优化方面有深厚经验的AI研究员，负责设计、评估和部署大规模机器学习模型的高性能推理系统。您将在模型架构、系统工程和硬件感知优化的交叉领域工作，提升真实生产环境中的延迟、吞吐量和成本效率。
主要职责
- 研究并开发优化大型神经网络推理性能的技术。
- 提升延迟、吞吐量、内存效率和每次推理的成本。
- 设计并评估模型级别的优化（量化、剪枝、KV缓存优化、架构感知简化）。
- 实现系统级别的优化（动态批处理、内核融合、多GPU推理、预填充与解码优化）。
- 在硬件加速器上对推理工作负载进行基准测试。
- 与工程团队合作部署优化后的推理流程。
- 将研究洞察转化为可投入生产的改进。
所需资格
- 在机器学习、深度学习或AI系统方面有扎实的背景。
- 有优化大规模模型推理的实际经验。
- 精通Python和现代机器学习框架（如PyTorch）。
- 有使用推理工具的经验（如Triton、TensorRT、vLLM、ONNX Runtime）。
- 能够设计实验并清晰地传达结果。
优选/加分资格
- 有在大规模场景下部署生产推理系统的经验。
- 熟悉分布式和多GPU推理。
- 有为开源机器学习或推理框架做出贡献的经验。
- 在机器学习、系统或相关领域发表过同行评审的研究论文。
- 有接近硬件的工作经验（CUDA、ROCM、分析工具）。
成功标准
- 延迟、吞吐量和成本效率的可衡量提升。
- 优化后的推理系统在生产环境中稳定运行。
- 研究思路成功转化为可部署的系统。
- 清晰的基准测试和文档，支持产品决策。
相关研究领域（加分项）
- 长上下文推理优化
- 推测性解码
- KV缓存压缩与分页
- 高效解码策略
- 硬件感知推理设计
最初发布于喜马拉雅斯
查看英文原文
Role Overview
We are seeking an AI Researcher with deep experience in inference optimization to design, evaluate, and deploy high-performance inference systems for large-scale machine learning models. You will work at the intersection of model architecture, systems engineering, and hardware-aware optimization, improving latency, throughput, and cost efficiency across real-world production environments.
Key Responsibilities
- Research and develop techniques to optimize inference performance for large neural networks.
- Improve latency, throughput, memory efficiency, and cost per inference.
- Design and evaluate model-level optimizations (quantization, pruning, KV-cache optimization, architecture-aware simplifications).
- Implement systems-level optimizations (dynamic batching, kernel fusion, multi-GPU inference, prefill vs decode optimization).
- Benchmark inference workloads across hardware accelerators.
- Collaborate with engineering teams to deploy optimized inference pipelines.
- Translate research insights into production-ready improvements.
Required Qualifications
- Strong background in machine learning, deep learning, or AI systems.
- Hands-on experience optimizing inference for large-scale models.
- Proficiency in Python and modern ML frameworks (e.g., PyTorch).
- Experience with inference tooling (e.g., Triton, TensorRT, vLLM, ONNX Runtime).
- Ability to design experiments and communicate results clearly.
Preferred / Nice-to-Have Qualifications
- Experience deploying production inference systems at scale.
- Familiarity with distributed and multi-GPU inference.
- Experience contributing to open-source ML or inference frameworks.
- Authorship or co-authorship of peer-reviewed research papers in machine learning, systems, or related fields.
- Experience working close to hardware (CUDA, ROCm, profiling tools).
What Success Looks Like
- Measurable gains in latency, throughput, and cost efficiency.
- Optimized inference systems running reliably in production.
- Research ideas successfully translated into deployable systems.
- Clear benchmarks and documentation that inform product decisions.
Relevant Research Areas (Bonus)
- Long-context inference optimization
- Speculative decoding
- KV-cache compression and paging
- Efficient decoding strategies
- Hardware-aware inference design
Originally posted on Himalayas