TL;DR
Benchmark Engineer (Backend): Design and maintain reproducible benchmarks and tooling for vector search and distributed workloads with an accent on performance evaluation, correctness, and transparency. Focus on building realistic benchmarks, analyzing performance trade-offs, and communicating results to inform product decisions and user trust.
Location: Fully remote, global
Company
Qdrant is an open-source vector database powering high-performance similarity search and AI applications worldwide.
What you will do
- Design and maintain reproducible benchmarks for vector search, indexing, filtering, and distributed workloads
- Evaluate performance metrics including latency, throughput, recall, memory usage, and cost
- Compare Qdrant against alternative solutions in a fair and transparent manner
- Build and maintain benchmarking tooling, datasets, and automation pipelines
- Collaborate with core engineers to identify regressions and optimization opportunities
- Translate benchmark results into clear narratives for documentation and presentations
Requirements
- Strong software engineering skills in Rust, Python, Go, or similar languages
- Solid understanding of databases, distributed systems, or search engines
- Experience with performance testing, profiling, and benchmarking
- Ability to reason about trade-offs such as speed vs accuracy and memory vs latency
- Comfort working with large datasets and automation pipelines
- Clear communication skills to explain data and implications
Nice to have
- Experience with vector search, ANN algorithms, or ML infrastructure
- Familiarity with cloud environments and containerized workloads
- Open-source project contributions
- Knowledge of observability tools and performance profiling
Culture & Benefits
- Work on core infrastructure for modern AI systems
- Open-source, engineering-driven culture
- Fully remote team with flexible working hours
- High ownership and real impact
- Opportunity to shape industry standards for vector databases