Senior Solutions Engineer (Bay Area)

Most SE roles are demo cycles. This one is different.

Qdrant just closed a $50M Series B to build the retrieval infrastructure layer for modern AI. Our stack (written in Rust, deployed from edge devices to billion-scale air-gapped clusters) is used by engineering teams at Canva, HubSpot, Tripadvisor, and Bosch. As we expand our Bay Area field presence, we're looking for a Senior Solutions Engineer who wants to be the technical authority in deals that determine how enterprises build AI infrastructure for the next decade.

You'll work directly with Staff Engineers and CTOs, architect real systems, and own the technical outcome of every engagement you touch.

• Own the technical strategy in enterprise deals from first call to production deployment, partnering with Account Executives to qualify, architect, and close


• Design vector search architectures for high-scale workloads, including multi-tenant agentic systems, hybrid search pipelines, and low-latency retrieval at billion-vector scale


• Build proof-of-concept systems that customers take to production (not throw away), demonstrating Qdrant's performance advantages over JVM-based or proprietary alternatives


• Serve as a trusted advisor on AI infrastructure decisions, helping customers navigate migration from legacy databases, avoid architectural lock-in, and deploy across cloud, on-prem, or air-gapped environments


• Contribute to the field engineering knowledge base: reference architectures, technical guides, and reusable POC frameworks that scale the team's impact


• Represent Qdrant at Bay Area technical events, on whiteboards with engineering teams, and in online communities where the next wave of AI builders work

REQUIRED QUALIFICATIONS

• 5 years in solutions engineering, data infrastructure, or systems architecture with hands-on technical depth


• Experience designing or operating distributed systems, search infrastructure, or data pipelines at production scale.


• Ability to connect technical decisions to business outcomes and influence deal strategy alongside a commercial team


• Strong communicator across audiences: you can go deep on indexing tradeoffs with a Staff Engineer and explain infrastructure ROI to a CIO in the same afternoon


• Familiarity with the modern AI stack: LLMs, RAG pipelines, embedding models, or agentic frameworks is a strong plus


• Actively building or evaluating AI-native systems, not just following the space

Nice to Have

• Experience with vector search, approximate nearest neighbor algorithms, or semantic retrieval systems


• Background with Rust, C , or other systems languages


• Familiarity with deployment patterns: Kubernetes, hybrid cloud, on-prem, or air-gapped environments

• Equity with meaningful upside in a well-capitalized, fast-scaling company


• Medical, dental, and vision coverage


• Hardware budget: choose your own setup


• Annual learning and conference budget


• Flexible hours, async-friendly culture, and regular team offsites


• Remote-first team with the expectation of Bay Area presence for customer meetings

Recruiting Agencies and Headhunters, please only via 𝗵𝘁𝘁𝗽𝘀://𝗵𝗶𝗿𝗲𝗯𝘂𝗳𝗳𝗲𝗿.𝗰𝗼𝗺?ref=qdrant