You ll profile the existing codebase, design a GPU strategy (CUDA/SYCL/OpenMP offload), implement kernels, and validate numerical parity against CPU baselines. You ll use modern AI coding tools (Cursor, GitHub Copilot) to speed up refactors paired with rigorous reviews and benchmarking to ensure quality.

What You ll Do

Assess Plan:
- Build and run the current C/C++ code; map dependencies and hot paths.
- Create a migration plan (targets, expected speedups, parity criteria, risks).
Accelerate:
- Implement GPU kernels and parallel patterns (tiling, shared memory, coalesced access).
- Choose and justify the approach:
  CUDA
  (preferred),
  SYCL/oneAPI
  ,
  HIP
  , or
  OpenMP offload
  based on portability needs.
- Optimize memory layout (AoS SoA), minimize host device transfers, and exploit libraries (e.g., cuBLAS/cuFFT/Thrust when applicable).
Validate Benchmark:
- Build a CPU GPU parity test harness with numerical tolerances; develop unit/integration tests.
- Benchmark with representative Radio Planning workloads; iterate using Nsight/VTune/rocprof.
- Document speedups, bottlenecks, and trade-offs.
Engineer for Maintainability:
- Add comments, diagrams, and a
  GPU Migration Guide
  (build/run, architecture, profiling notes).
- Set up CI jobs for linting (clang-tidy), tests, and benchmark smoke checks.
- Use
  Cursor
  and
  GitHub Copilot
  responsibly to draft code/tests, then refine via manual review.

Minimum Qualifications

Strong
C/C++
skills and comfort reading legacy code.
Coursework or hands-on experience with
GPU programming
(CUDA, SYCL, HIP, or OpenMP offload).
Familiarity with
parallel computing
concepts (threads, warps, memory hierarchy, vectorization).
Experience with
profiling optimization
(e.g., Nsight Compute/Systems, nvprof, perf, VTune).
Comfortable with
CMake
,
Git
, and unit testing (e.g., GoogleTest).
Practical use of
Cursor
and/or
GitHub Copilot
(or similar AI pair-programming tools).

Nice to Have

Knowledge of
numerical methods
/linear algebra; experience with
cuBLAS/cuFFT/Thrust
.
Exposure to
telecom/RF propagation
or scientific computing on large grids.
Python for quick experiments/plots; basic PyTorch/NumPy for validation scripts.
Docker/Containers for reproducible dev; understanding of licensing and IP hygiene for AI-assisted code.

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