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Develops and optimizes AI frameworks like SGLang, profiling distributed deep learning models to improve performance across hardware backends.
Build and maintain Python-based backend systems, AI workflows, and React UIs for Proofpoint’s cybersecurity platform, handling data capture, compliance, and LLM-powered investigations.
Build and optimize ML deployment platforms and inference pipelines for autonomous-vehicle software, shipping PyTorch models to GM’s Super Cruise fleet with real-time latency and safety constraints.
Own the AI inference performance roadmap at NVIDIA, turning deep optimization techniques into products that improve latency, efficiency, and cost per token across the inference stack for LLM deployments.
Memwize is a VC-backed, stealth-mode startup building rack-level AI inference systems . We are seeking an experienced Senior/Principal Machine Learning Engineer to drive performance improvements in inference frameworks…
Build and deploy production-grade AI systems, including RAG pipelines and agent frameworks, to power a sustainability-focused product lifecycle management platform.
Optimize and port AI inference/training kernels across NVIDIA, AMD, TPUs, and emerging accelerators, designing portable abstractions for SGLang and Miles.
Engineer and optimize GPU clusters for AI training and inference, build scheduling/orchestration systems, and improve performance, cost, and reliability of AI infrastructure.
Optimize AI model inference for low-latency production deployment, manage Kubernetes-based ML infrastructure, and collaborate with data-science teams to scale and automate systems handling thousands of concurrent requests.
Principal engineer leading backend and ML infrastructure at a Series-C conversational AI company, scaling real-time speech/NLP systems, GPU fleets, and inference pipelines for enterprise contact centers.
业务方向： 1、开发高性推理引擎及相关核心组件，根据硬件特性和 AI 模型的特点，不断迭代和完善，持续提升推理性能。 2、开发分布式 KVCache 存储组件，负责 KVCache 生命周期管理，包括显存、内存、SSD 的分层和缓存策略优化。 3、分析和优化推理阶段的性能瓶颈，进行系统级调优，建设并完善性能剖析和优化工具链，沉淀性能优化方法论。 工作范式： 我们正在打造一个 AI-Native（原生 AI） 的研发环境，将 AI…
Build AI agents and automation scripts for an internal AI-powered software engineering platform, integrating LLMs and testing tools to improve developer workflows.
Senior DevOps Engineer to design, build, and operate scalable infrastructure for an enterprise AI platform, using Kubernetes, cloud providers, and CI/CD pipelines.
Builds and optimizes high-performance ML systems for TPU hardware using JAX, XLA, and Pallas, focusing on inference/training workloads and compiler/runtime tuning.
Builds and optimizes large-scale AI inference systems for frontier models, focusing on performance, latency, and cost across thousands of GPUs.
Principal Architect leads the design of a GPU-powered simulation platform for AI data centers, enabling real-time power orchestration and validation of energy-aware compute systems.
Builds and maintains developer-facing tools (APIs, SDKs, dashboards) on top of AI inference/training infrastructure like SGLang and Miles, collaborating with product and research teams to create intuitive interfaces.
Optimize and accelerate LLM inference and training systems like SGLang and Miles by profiling GPU performance, writing custom kernels, and enabling new models on modern hardware.
Build and ship production-grade AI systems—LLMs, agents, retrieval, and evals—from prototype to scalable deployment for high-stakes decision-making.
Design and build confidential AI systems that protect models and data using trusted execution environments, remote attestation, and secure provisioning in cloud-native and on-prem deployments.
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