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Redwood City, California, United States
Onsite
Zilliz is a fast-growing startup developing the industry’s leading vector database for enterprise-grade AI. Founded by the engineers behind Milvus, the world’s most popular open-source vector database, the company builds next-generation database technologies to help organizations quickly create AI applications. On a mission to democratize AI, Zilliz is committed to simplifying data management for AI applications and making vector databases accessible to every organization.
As a Solutions Architect at Zilliz, you'll help AI startups and digital-native enterprises design, validate, and operate production retrieval systems on Zilliz Cloud (fully-managed Milvus). Working with engineering leaders and hands-on builders, you'll turn product goals into data models, search architectures, benchmarks, and deployment plans across RAG, hybrid and multimodal search, recommender systems, agent memory, and semantic analytics workloads.
This is a deeply technical, customer-facing role. The strongest candidates move comfortably between a whiteboard and a profiler: they understand how schema, vector dimension, filter predicates, and indexes could affect latency, recall, throughput, reliability, and cost, as well as explain those tradeoffs to engineers and business leaders alike.
Partner with Account Executives on technical win strategy, leading discovery on use case, traffic pattern, latency requirements, and cost constraints
Design solutions covering data modeling, multi-tenancy, latency profile, full-text search, filtering. Guide POCs and benchmarks with clear success criteria. Guiding customers to make wise trade-offs on performance, quality and cost. Translate customer growth plans into deployment and capacity strategies
Partner with engineering to troubleshoot production issues, driving customer communication, and follow up with clear root-cause analysis and durable remediation
Build trusted relationships with founders, engineering executives, ML leaders, and developers from evaluation through production and expansion
Turn field learning into reusable demos, reference architectures, and product feedback; engage with Milvus open-source community and shape the roadmap
5+ years in solutions architecture, solutions/customer engineering, or a comparable technical customer-facing role
Strong foundation in distributed systems and computer science, with experience in schema design, performance benchmarking, and troubleshooting with metrics and logs
Ability to reason about tradeoffs among performance, reliability, operational complexity and cost. Strong business judgment and customer empathy: you can distinguish the needs of a fast-moving startup, a scaled platform, and a regulated enterprise, and adapt accordingly
Excellent written and verbal communication, including the ability to challenge assumptions constructively and lead executive and engineering conversations
Strong sense of ownership, intellectual curiosity, and comfort in a fast-changing startup environment
Experience with distributed databases, large-scale data pipelines or lakehouses.
Experience with search stack, including embedding model, full-text search, ranking and search quality.
Experience with major cloud platforms or kubernetes
Customer outcomes first: We start with the customer's workload and constraints, then recommend the simplest architecture that can scale.
Deep and evidence-driven: We value correctness, benchmarks, and clear reasoning over slogans, and we don't hide uncertainty.
Fast and self-learning: We're engineers at heart, not afraid to dive into code to solve customer problems and use AI to self-educate.
One team: We share field learning across Sales, Engineering, and Product, and take ownership through resolution.
Zilliz is an Equal Opportunity Employer and welcomes people from all backgrounds, experiences, abilities, and perspectives. All qualified applicants will receive consideration for employment regardless of race, color, national origin, religion, sexual orientation, gender, gender identity, age, physical disability, or length of time spent unemployed.