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Mid-level AI developer building and integrating locally hosted LLMs and ML models into classified government systems, focusing on secure, air-gapped environments and production-grade delivery.
Build and optimize production-grade AI systems using RAG pipelines, vector stores, and cloud-native tools to deliver secure, scalable solutions for federal environments.
Обязанности: Разрабатывать системы анализа здоровья: наше ключевое направление AI, который помогает пользователю понять своё состояние и вовремя дойти до врача; Внедрять AI-фичи в продукт и в компанию: интеграция LLM…
Build and deploy quantized large language models for in-vehicle AI, focusing on PTQ, QAT, and low-bit inference to ensure numerical consistency and performance on XPENG’s Turing AI chip.
Build and deploy quantized large language models for in-vehicle AI, focusing on PTQ, QAT, and low-bit inference to optimize performance on XPENG’s Turing AI chip.
Builds and optimizes inference stacks for large-scale Apple foundation models, enabling AI features across services like Siri and Photos with low latency.
Senior ML engineer builds NLP and computer-vision models to automate returns, disputes and call-center reviews for a large marketplace.
1.主导设计和研发下一代分布式多级分层缓存加速系统，基于 GPU 主机本地内存、NVMe SSD、远端并行文件系统构建大容量弹性扩缩容的多级缓存架构，实现数据按需与透明加速； 2.参与设计和研发百 PB 规模的分布式文件缓存系统，支撑自动驾驶、大语言模型等业务的大规模分布式 AI 训练集群； 3.设计并实现智能数据放置与生命周期管理策略（如基于访问热度的分层调度、TTL/LRU 淘汰、预取优化），降低 GPU 因存储 I/O…
Build and operate secure Kubernetes-based platforms for AI-assisted software development, including GitOps, private registries, and observability, ensuring security and auditability in sovereignty-sensitive environments.
Build and maintain automated QA flows and benchmarks for AI frameworks like PyTorch and vLLM, validating performance across hardware and integrating tests into CI/CD pipelines.
Develop and optimize AI frameworks like vLLM and PyTorch, focusing on distributed algorithms and performance tuning for deep learning models across hardware backends.
Develops and optimizes AI frameworks like SGLang, profiling distributed deep learning models to improve performance across hardware backends.
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.
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