Staff Engineer, MongoDB

• 8+ years of experience in data management systems or related distributed infrastructure
• Deep proficiency in modern programming languages and techniques, e.g., Java, Rust
• Demonstrable experience designing and operating distributed systems, cloud services, or SaaS products at scale
• The ability to reason about systems at the physical layer: data consistency, durability guarantees, concurrency, and failure modes in distributed environments
• A track record of operating at Staff or Principal scope: defining technical direction, resolving cross-team ambiguity, and personally championing initiatives from conception to delivery
• Operational Architects. You understand the physical limits of the stack. You can articulate why a system breaks at 100x load, reason about the risks of a dual-write migration strategy, and make data consistency and durability decisions with conviction
• Scale-First Thinkers. You identify risks before they surface. You think in migration paths, failure modes, and second-order architectural consequences, not just implementation details
• Technical Force Multipliers. You create leverage across teams. You challenge requirements rather than execute them, set technical direction independently, and hold a high bar for architectural clarity whether you are mentoring a struggling engineer or reviewing a design with a principal
• Systems Intuitionists. You connect operational constraints to design decisions in concrete terms. You know why fsync matters. You know how atomic operations behave under contention. You have built systems where these things were not hypothetical

Desirable

• The Search Query team at MongoDB builds the core infrastructure behind MongoDB Search and Vector Search
• Our mission is to make advanced search capabilities feel native to the database, so developers can build powerful, scalable applications without standing up separate systems or compromising transactional performance
• We are the team behind mongot, the indexing and query execution engine that powers Search and Vector Search across MongoDB Atlas and self-managed deployments
• Our work sits at the intersection of distributed systems, databases, and search infrastructure
• We integrate Apache Lucene with MongoDB using native query operators like $search and $vectorSearch, build asynchronous change-stream-driven indexing pipelines that scale independently from transactional workloads, and support deployments across cloud, on-prem, and hybrid environments
• Engineers on this team own meaningful subsystems, influence architectural decisions, and work on core database technology used by developers globally
• This is a rare opportunity to set the technical direction for one of MongoDB's most strategic investments
• You will harden our infrastructure by implementing advanced resource management, admission control, and system safeguards to ensure high availability
• You’ll serve as a force multiplier for the Search and Vector teams, building the foundational tooling, observability, and safeguards needed to ship high performance systems at scale
• Own the cross-system data infrastructure layer for MongoDB's next-generation Search and Vector Search capabilities, carrying outcomes that span 6-18 months
• Define the technical roadmap for Atlas Search, identifying gaps, proposing solutions, and driving alignment across engineering teams globally
• Lead the architecture and evolution of the core mongot and MongoDB infrastructure, making durability, consistency, and scale decisions with conviction
• Drive complex, multi-team initiatives by building technical consensus, navigating ambiguity, and setting direction when there is no clean playbook
• Serve as a trusted technical partner to leadership on roadmap, architecture, and engineering process, advising rather than just informing
• Raise the technical ceiling of the team through mentoring, rigorous code review, and knowledge-sharing that creates leverage beyond your own output
• Standardize how engineering teams across the globe contract with one another, reducing coordination costs and increasing execution velocity at scale