Key responsibilities include:

• Design and building of tooling and automated processes for provisioning and execution of Elasticsearch performance benchmarking jobs at scale.
• Defining methodologies and guidelines for performance benchmarking servicing development teams and solution architects.
• Play a lead role in cross-functional team efforts and engagements.
• Mentoring and advising team members on proposed technical solutions.
• Developing reporting for and interpreting of benchmarking results.
• Investigate and assist in resolving performance escalations and capacity planning issues.
• Address and devise solutions for benchmarking disparate use cases and workloads.

Requirements include:

• Software development experience with Python and Linux shell required (Expert Level).
• Familiarity with Linux system-level performance measurement and associated tooling.
• Prior experience in the troubleshooting and root cause analysis of performance issues in complex distributed architectures.
• Prior experience with CI/CD pipelines and automating process workflows.
• Experience in cloud architectures, service models, and operating across multiple cloud service providers, assessing tradeoffs from both performance and deployment perspectives.
• Ability to multitask, effectively manage your time, and collaborate with team members globally across time zones.
• Familiarity with measuring performance and sizing in Linux VM and container environments.
• Experience with performance tuning of Java applications and JVMs.

Bonus points for prior experience with large, distributed search engines and in developing and tuning queries, familiarity with the Elasticsearch Query API and building Kibana dashboards, cloud orchestration with Kubernetes, and experience applying the use of Artificial Intelligence (AI) for devising and expanding benchmarking practices and enhancing benchmarking tooling.

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Responsibilities

• Define the long-term vision and roadmap for Alluxio's AI data platform, covering inference, training, and agentic workloads.
• Collaborate with engineering to design features that deliver high-throughput, low-latency data access (e.g., GPU-aware caching, streaming reads, tiered prefetching).
• Ensure seamless integration with frameworks like PyTorch, TensorFlow, Ray, and Triton; evolve Alluxio's APIs for AI-native workloads.
• Engage directly with enterprise AI teams to understand workload patterns, validate impact, and prioritize roadmap direction.
• Stay ahead of trends in multi-model serving, retrieval-augmented generation (RAG), and agentic orchestration; translate them into actionable product plans.

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.
• 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.

Benefits

• 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.

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