Senior Software Engineer (YDB Team)

Accountabilities

• • Develop and optimize YDB components to take full advantage of modern hardware, including QLC NVMe drives, high-bandwidth network adapters, and DPUs.
  • Improve performance across widely used storage technologies, including HDDs and TLC NVMe devices.
  • Reengineer system components using more efficient algorithms to address complex scalability, performance, and reliability challenges.
  • Design and implement high-performance, low-latency software components for heavily loaded production systems.
  • Analyze system behavior and identify performance bottlenecks using profiling, debugging, and diagnostic techniques.
  • Work on distributed storage and database infrastructure supporting demanding AI and cloud workloads.
  • Contribute to the reliability, availability, and durability of production-grade distributed systems.
  • Participate actively in incident investigation and resolution, helping identify root causes and implement lasting improvements.
  • Collaborate with engineers across infrastructure, storage, networking, and AI-focused teams to deliver robust technical solutions.
  • Contribute to an open-source codebase and participate in technical discussions, design decisions, and continuous improvement.
  • Take ownership of engineering challenges from investigation and design through implementation, optimization, testing, and production deployment.

  Requirements

  • 5+ years of professional experience developing in C or C++ for highly loaded, performance-critical systems.
  • Strong understanding of low-level systems concepts, including CPU caches, modern CPU atomic operations, and NUMA architectures.
  • Proven experience developing high-performance and low-latency software components.
  • Ability to investigate complex production issues using core dumps, flame graphs, sanitized builds, and related debugging techniques.
  • Strong systems programming and performance optimization skills, with an analytical approach to diagnosing bottlenecks.
  • Experience with on-disk data structures such as LSM trees or B+ trees is a strong advantage.
  • Hands-on experience with tools such as perf, VTune, bpftrace, or gdb is desirable.
  • Knowledge of storage algorithms, including erasure coding and checksumming, is a plus.
  • Understanding of storage device internals, particularly NVMe and HDD technologies.
  • Familiarity with Linux kernel development and technologies such as SPDK, DPDK, libaio, or io_uring is advantageous.
  • Knowledge of networking concepts and protocols including IP, TCP, UDP, and DNS; experience with InfiniBand, RoCE, or RDMA is a plus.
  • Experience with Kubernetes and Grafana is desirable.
  • Experience designing production-grade distributed storage components and understanding availability and durability calculations is highly valued.
  • Demonstrated ability to troubleshoot complex systems, participate in incident resolution, and work effectively in collaborative engineering environments.
  • Strong ownership, problem-solving skills, and enthusiasm for tackling technically challenging infrastructure problems.
  • Willingness to participate in coding interviews as part of the recruitment process.
  • Candidates must be authorized to work in the country where they apply.

  Benefits

  • Competitive compensation.
  • Career growth and continuous learning opportunities.
  • Flexibility and significant ownership over your work.
  • Opportunity to work on impactful infrastructure supporting advanced AI workloads.
  • Collaborative, innovative, and technically driven working culture.
  • International environment with highly experienced engineering and research teams.
  • Opportunity to work with cutting-edge compute, storage, networking, and distributed systems technologies.
  • Meaningful opportunity to influence the performance and future direction of large-scale AI infrastructure.
  • Inclusive workplace committed to equal employment opportunities.
  • Support and reasonable accommodations available during the application process.