USATechnical Product Manager
1 г. назад
USAHybrid
ai infrastructuredata orchestrationdistributed systemsgpu efficiency
Define and execute AI systems strategy bridging AI infrastructure and distributed data systems to improve latency, throughput, and GPU efficiency for AI model inference and training.
О компании
- About : powers the data layer for modern AI and analytics. Proven in production at eight of the top ten internet companies and seven of the ten highest-valued enterprises globally, ’s open-source data-orchestration platform unifies data across storage systems, regions, and clouds — enabling memory-speed access for large-scale AI and analytics workloads. Spun out of UC Berkeley’s AMPLab and backed by Andreessen Horowitz, Hillhouse Capital, and Seven Seas Partners, sits at the intersection of data, distributed systems, and AI infrastructure . Our technology is deployed at scale by Meta, Uber, TikTok, Alibaba, Microsoft, and Salesforce , orchestrating data for billions of operations per day. Learn more at .io or on Wikipedia. The Role We’re hiring a Techni
Условия
- Shape how the world’s most advanced AI systems access and process data.
- Work at the intersection of distributed systems, AI acceleration, and open source .
- Collaborate with world-class engineers, researchers, and customers driving the AI frontier.
- Competitive compensation and equity package with comprehensive benefits.
- A culture built on curiosity, empathy, and deep technical rigor.
Другое
- AI Product Strategy — define the long-term vision and roadmap for ’s AI data platform, covering inference, training, and agentic workloads.
- Systems Optimization for AI — collaborate with engineering to design features that deliver high-throughput, low-latency data access (e.g., GPU-aware caching, streaming reads, tiered prefetching).
- AI Ecosystem Integration — ensure seamless integration with frameworks like PyTorch, TensorFlow, Ray, and Triton; evolve ’s APIs for AI-native workloads.
- Customer & Partner Collaboration — engage directly with enterprise AI teams to understand workload patterns, validate impact, and prioritize roadmap direction.
- Market Awareness & Foresight — stay ahead of trends in multi-model serving, retrieval-augmented generation (RAG), and agentic orchestration; translate them into actionable product plans.
- 5–9 years of experience in product management or technical leadership within AI infrastructure, ML platforms, or distributed systems.
- Strong understanding of AI/ML workflows — from model training and deployment to inference and data-access pipelines.
- Proven track record of delivering infrastructure features that improve latency, GPU utilization, or total cost of ownership .
- Technical fluency with distributed systems, caching, and cloud orchestration (Kubernetes, AWS/GCP/Azure).
- Familiarity with AI frameworks such as PyTorch, TensorFlow, Triton, Ray, or LangChain .
- Exceptional communication and strategic thinking — ability to translate complex systems work into clear, prioritized roadmaps.