Alluxio
Technical Product Manager
Foster City, California · Full-time
Sponsorship not specified$190k-$240kDetected 334 days ago
Distributed SystemsAWSGCPAzureKubernetesMachine LearningTensorFlowPyTorchRAGAgentic AIProduct ManagementProduct StrategySalesforceResearchLeadershipCommunicationCollaboration
About the role
- We're hiring a Technical Product Manager to define and execute Alluxio's AI systems strategy - spanning inference, training, and emerging agentic workloads.
- This role bridges the worlds of AI infrastructure and distributed data systems, guiding how Alluxio evolves to serve next-generation model architectures and large-scale data flows.
- AI Product Strategy - define the long-term vision and roadmap for Alluxio's AI data platform, covering inference, training, and agentic workloads.
Responsibilities
- 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).
- Customer & Partner Collaboration - engage directly with enterprise AI teams to understand workload patterns, validate impact, and prioritize roadmap direction.
Requirements
- 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.
- 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.
Compensation
- Competitive compensation and equity package with comprehensive benefits.
Apply directly at Alluxio →Create a free account for alerts like thisView Alluxio immigration profile
This listing is sourced directly from Alluxio's careers page and normalized into a canonical job model.