Hybrid CUDA Kernel Engineer

at Pragmatike

• Job title: CUDA Kernel Engineer
• Job type: Full Time
• Experience level: Mid level, Senior
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: Distributed Systems
• Location requirements: Hybrid, Cambridge, United States

About the role

• CUDA Kernel Engineer optimizing NVIDIA CUDA kernels for AI systems used by Fortune 500 clients. Collaborating with teams to enhance GPU performance in a fast-growing AI startup.

Responsibilities

• Design, implement, and optimize custom CUDA kernels for NVIDIA GPUs, with a focus on maximizing occupancy, memory throughput, and warp efficiency.
• Profile GPU workloads using tools such as Nsight Compute, Nsight Systems, nvprof, and CUDA‐MEMCHECK.
• Analyze and eliminate performance bottlenecks including warp divergence, uncoalesced memory access, register pressure, and PCIe transfer overhead.
• Improve GPU memory pipelines (global, shared, L2, texture memory) and ensure proper memory coalescing.
• Collaborate closely with AI systems, model acceleration, and backend distributed systems teams.
• Contribute to GPU architecture decisions, kernel libraries, and internal performance-engineering best practices.

Requirements

• Proven track record building NVIDIA CUDA kernels from scratchnot just calling existing libraries.
• Strong ability to optimize kernels (tiling strategies, occupancy tuning, shared memory design, warp scheduling).
• Deep understanding of CUDA threads, warps, blocks, and grids, GPU memory hierarchy and memory coalescing, as well as warp divergence (how to detect, analyze, and mitigate it)
• Experience diagnosing PCIe bottlenecks and optimizing host-device transfers (pinned memory, streams, batching, overlap).
• Familiarity with C++, CUDA runtime APIs, and GPU debugging/profiling tooling.

Benefits

• Competitive salary & equity options
• Sign-on bonus
• Health, Dental, and Vision
• 401k