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About the Role Sanas is looking for a Member of Technical Staff to lead the post-training and deployment of large language models across a new generation of self-hosted, sovereign-deployed products. This is a rare…
Build and deploy production-grade agentic AI and LLM applications for APAC enterprises, including RAG pipelines, multi-agent systems, and ML services across cloud and air-gapped environments.
Build and deploy LLM-powered agents and RAG systems using Python, FastAPI, and vector databases; optimize token costs and model performance for scalable AI applications.
Optimize AI training and inference workloads for speed, cost, and efficiency across the full stack, from GPU kernels to distributed systems, using Python, C++, and profiling tools.
Design and operate high-performance AI inference platforms, focusing on systems engineering for model serving, routing, caching, and autoscaling in production.
Build and maintain Python-based backend systems for cybersecurity data capture, compliance workflows, and AI-powered threat classification in a high-reliability environment.
Build and lead backend systems in Python for a cybersecurity platform that captures communications data, processes events, and integrates AI classification and agentic workflows across enterprise tools like Microsoft and Slack.
Build and deploy agentic AI systems for financial data, focusing on LLM orchestration, retrieval, and scalable workflows to power generative AI applications in finance.
Build and deploy ML/LLM models, AI agents, and MLOps pipelines to power Ancestry’s family-history platform and GenAI features.
Build and train large language models from scratch for coding assistance, deploying them to production across JetBrains’ developer tools ecosystem.
Build and maintain MLOps infrastructure and pipelines to streamline AI model development, training, and deployment for JetBrains' developer tools and AI-powered IDE features.
Build and deploy AI/ML systems for B2B financial operations, focusing on invoice matching, payment reconciliation, and agentic workflows using LLMs and RAG in a fintech SaaS platform.
Build and maintain the AI platform and data lakehouse, automating operations and deploying services using Python, Kubernetes, and MLOps tools.
Build benchmarking, forecasting, and data-analysis systems for Apple’s AI inference platform to optimize performance and capacity at massive scale.
Build and maintain the AI platform and data lakehouse, automating operations and integrating services with APIs and cloud infrastructure using Python, Kubernetes, and MLOps tools.
The Data-AML-Engine Orchestration team builds large-scale machine learning infrastructure that powers online model serving across ByteDance products, including TikTok. We develop the orchestration, scheduling, and…
1、探索下一代Agent：研究Agent在复杂长程任务（Long-horizon Tasks）中的能力边界；探索Planning、Memory、Tool Use、Multi-Agent、Proactive Agent等方向；构建面向真实生产环境的大规模Agent系统； 2、大模型后训练（Post-training）：基于SFT、Preference Learning、RL（GRPO、PPO等）持续提升模型能力；构建Agent…
Principal engineer leading backend and ML infrastructure at a Series C conversational AI company, designing real-time data pipelines, scaling GPU fleets, and optimizing inference costs for enterprise speech/NLP systems.
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