Developer - I/O Acceleration

As an Engineer on I/O Acceleration, you will own and optimize the path data travels from disk to GPU memory. Your work will directly determine how many queries per dollar Gala Lakehouse can deliver on Blackwell - and whether modern analytics workloads run GPU-bound (where they should) or I/O-bound (where they are today).

What You'll Do

• Design, build, and optimize accelerated I/O and decompression paths for data-intensive analytics workloads.

• Improve end-to-end throughput across the storage * network * host * GPU boundary, eliminating copies, syscalls, and stalls.

• Integrate with GPU-aware runtimes and high-bandwidth fabrics (GPUDirect Storage, RDMA, NVMe-oF) and tune for Blackwell-class hardware.

• Build benchmarks and microbenchmarks that expose I/O cliffs, queue contention, and tail latency under realistic query mixes.

• Instrument the data path so cost-per-query, bandwidth-per-GPU, and CPU overhead are first-class, observable metrics.

• Collaborate with the query engine, storage, and hardware teams to co-design APIs that make accelerated I/O usable, not just possible.

Required technical and professional expertise

• Strong modern C++ and deep comfort with Linux systems internals (page cache, O_DIRECT, io_uring, NUMA, scheduling).

• Hands-on experience in at least one of: storage I/O subsystems, decompression and codec implementation, or query-engine data paths.

• Working knowledge of GPU-aware pipelines or adjacent acceleration frameworks (CUDA, GPUDirect, or similar).

• Strong performance-profiling and bottleneck-isolation skills - you can read a flame graph, an nsys trace, and an fio result and know what to do next.

• Familiarity with distributed data systems and the realities of running them at scale.

• Track record of delivering production software in Agile, collaborative environments, including contributing to automated CI/CD pipelines.

Preferred technical and professional experience

• Production experience with GPUDirect Storage, RDMA, or NVMe-oF integrations.

• Exposure to ESS6000, Lustre, GPFS, or other parallel and clustered file systems.

• Track record of large-scale benchmarking, and publishing or presenting performance results.

• Contributions to open-source data, storage, or GPU-runtime projects (Arrow, cuDF, Velox, DuckDB, Spark, and similar).