Database Research Scientist

ClickHouse Labs is an applied research group that works closely with engineering teams to identify challenging problems in database systems and develop novel solutions. Our goal is to advance the state of the art and translate research ideas into real-world system improvements.

About the Role

As a Research Scientist at ClickHouse Labs, you will work on high-impact problems at the intersection of database systems, distributed systems, AI, and systems software engineering while collaborating with experienced researchers and engineers. The role covers a broad range of problems across database systems research and engineering, including but not limited to query optimization, execution engines, distributed systems, consensus protocols, materialized views, indexing and data structures, benchmarking, workload analysis, and emerging agentic workloads.

In this role, you will work on leading open-source database systems (e.g., ClickHouse, PostgreSQL). You will investigate performance and scalability bottlenecks in production-quality systems, develop new algorithms and system designs, and evaluate them under realistic workloads and hardware configurations. Projects may involve improving query planning and execution, reducing CPU and memory overhead, increasing throughput and concurrency, scaling across distributed deployments, or redesigning system components to leverage modern hardware better. You will work closely with engineers to turn successful ideas into robust implementations that can be deployed and maintained in widely used open-source systems.

Researchers are encouraged to publish their work, contribute to open-source projects, participate in leading research conferences, and pursue ambitious projects with strong engineering and research support.

Responsibilities

Conduct applied research aimed at advancing the performance, scalability, reliability, and functionality of open-source database management systems, including ClickHouse and PostgreSQL.

Design and evaluate new algorithms, data structures, and system architectures for areas such as query optimization, indexing, storage management, query execution, and distributed data management.

Develop high-quality research prototypes and production-grade implementations, and work with engineering teams to integrate successful techniques into open-source and production systems.

Collaborate closely with database engine, infrastructure, and data-plane engineers to identify high-impact research problems and translate research results into practical system improvements.

Analyze large-scale database workloads, performance traces, benchmarks, and production telemetry to identify bottlenecks and optimization opportunities.

Design rigorous experiments and benchmarks to evaluate new techniques across realistic workloads, hardware configurations, and deployment environments.

Communicate research results through technical reports, open-source contributions, internal presentations, and publications at leading database and systems venues (e.g., SIGMOD, VLDB, CIDR).

Contribute to the broader technical direction of ClickHouse Labs by identifying emerging research opportunities and helping shape longer-term research projects.

Qualifications

Minimum Qualifications

Masters/Ph.D. in Computer Science, Data Science, or a related field, with research experience in databases, distributed systems, systems software, or machine learning systems.

Strong publication record in leading peer-reviewed research venues such as SIGMOD, VLDB, SOSP, OSDI, NSDI, or NeurIPS, with work relevant to databases or systems.

Strong programming and systems-development background, including experience working with database internals or other large systems codebases such as PostgreSQL, MySQL, ClickHouse, or comparable modern DBMSs.

Proficiency in systems programming using C, C++, Rust, Zig, or a comparable low-level programming language.

Strong analytical, experimental, problem-solving, and technical communication skills.

Preferred Qualifications

Experience developing or operating database systems in cloud environments such as AWS, Google Cloud Platform, or Microsoft Azure.

Familiarity with large language models, reinforcement learning, learned optimization, or other techniques at the intersection of machine learning and systems.