NVIDIA
5G and 6G network simulation
SeniorGPUPropagationEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior GPU Propagation Engineer at NVIDIA. Skills: GPU ray-tracing, CUDA, real-time propagation engine. Lead the design and implementation of a real-time, GPU-accelerated propagation engine. Architect and implement a GPU ray-tracing engine that operates at two time scales (planning and real-time)”
What You'll Achieve.
producing both per-link channel characterisations and radio maps at the speed required by production RAN stacks; making sub-millisecond propagation updates feasible
Industry & Context.
reason about memory hierarchy, occupancy, and compute-vs-bandwidth trade-offs at the kernel level
What They're Looking For.
Must Have
PhD in computer graphics, high-performance computing, computational electromagnetics, or a closely related field (or equivalent experience), 8+ years of relevant experience, Hands-on proficiency with CUDA, Proficiency in at least one GPU ray-tracing framework (OptiX, Vulkan RT, Embree), Track record of writing production-quality GPU code, Proficiency in GPU-friendly spatial data structures (BVH, space-filling curves, hash maps), Ability to reason about memory hierarchy, occupancy, and compute-vs-bandwidth trade-offs at the kernel level, Working knowledge of electromagnetic wave propagation phenomena (reflection, transmission, diffraction, scattering) sufficient to implement and validate a propagation engine
Nice to Have
Experience with real-time or near-real-time ray-tracing engines shipping in production systems, Prior work on multi-GPU partitioning for ray-tracing or large-scale simulation workloads, Familiarity with 3GPP channel models and wireless network planning tools, Knowledge of geospatial coordinate systems and tiling schemes
What You'll Do.
Lead the design and implementation of a real-time
GPU-accelerated propagation engine
Architect and implement a GPU ray-tracing engine that operates at two time scales (planning and real-time)
Produce volumetric radio maps (coverage
Deliver per-link multipath channel updates at millisecond cadence
Develop adaptive algorithms that exploit temporal coherence
Lay the foundations for real-time ray tracing on GPU hardware (algorithms
multi-GPU scaling strategies)
Full Job Description
We are seeking a self‑motivated senior engineer for the Aerial Omniverse Digital Twin team. This hire will lead the design and implementation of a real‑time, GPU‑accelerated propagation engine that predicts how radio signals travel through realistic 3‑D environments — producing both per‑link channel characterisations and radio maps at the speed required by production RAN stacks! This position offers the opportunity to work on foundational technology for 5G and 6G network simulation, using NVIDIA's world‑class GPU and ray‑tracing platforms. **What you 'll be doing:** As a member of NVIDIA's Aerial team, you will architect and implement a GPU ray‑tracing engine that operates at two time scales. At the planning scale, the engine produces volumetric radio maps — coverage, SINR, and best‑server maps at multiple resolutions, composable across cells, frequencies, and beam configurations — that network operators use to design and optimise deployments. At the real‑time scale, the same engine delivers per‑link multipath channel updates at the millisecond cadence that a production RAN stack requires, using adaptive algorithms that exploit temporal coherence to avoid recomputing what hasn't changed. Laying the foundations for real‑time ray tracing on GPU hardware — algorithms, data structures, and multi‑GPU scaling strategies that make sub‑millisecond propagation updates feasible — is the defining technical challenge of this role. **What we need to see:** * PhD in computer graphics, high‑performance computing, computational electromagnetics, or a closely related field (or equivalent experience). * 8+ years of relevant experience. * Hands‑on proficiency with CUDA and at least one GPU ray‑tracing framework (OptiX, Vulkan RT, Embree), with a track record of writing production‑quality GPU code. * Proficiency in GPU‑friendly spatial data structures (BVH, space‑filling curves, hash maps) and the ability to reason about memory hierarchy, occupancy, and compute‑vs‑bandwidth trade‑offs
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