Job Responsibilities
• Design,
implement, and maintain highly available, scalable, and fault-tolerant
distributed systems for graph data.
• Tackle
performance and scalability challenges, optimizing data ingestion, indexing,
and query pipelines for low-latency and high-throughput requirements. Conduct
systematic profiling and tuning.
• Build,
optimize, and operate our core vector embedding infrastructure to enable
efficient nearest neighbor search at scale.
• Proactively
diagnose, debug, and resolve complex issues across the entire data stack, from
performance bottlenecks and data inconsistencies to system failures. Lead root
cause analysis for production incidents and implement preventive measures.
Requirements
• Bachelor’s
degree in Computer Science or a related field
• 5 years of relevant experience
Skills and Knowledge
Deep, hands-on experience with one or more
vector databases or similarity search libraries.
• Proven
experience designing and working with any graph database and query languages
like Cypher
• Solid
understanding of distributed systems concepts: consensus, replication,
sharding, and fault tolerance.
• Solid
programming fundamentals; Expert-level proficiency in modern C++, with deep
understanding of language features and object-oriented design.
• Understanding
of distributed systems principles and the ability to evaluate trade-offs in
system design.
• Familiar
with Kafka, ETCD or similar technologies;
• Proactive
and collaborative team player with strong communication skills.
• Open
to adopting AI-assisted engineering practices ("vibe coding") to
improve productivity and code quality.
