Remote Image Processing Engineer Jobs in Santa Clara, CA

Job Description

• Work on real, hard rendering problems - light transport, physically-based materials, atmospheric and volumetric scattering, and sensor image formation - where physical correctness is the product.
• Own meaningful surface area: the sky/atmosphere system, the materials pipeline, the camera imaging pipeline, or the core ray tracing path, depending on your strengths.
• Influence the technical direction of a rendering platform used across the organization for perception training, closed-loop simulation, and reinforcement learning.
• Join a team that values rigor - papers, derivations, reference comparisons, and pixel-level regression testing - alongside pragmatic engineering.
  

• Advance the light transport in our OptiX-based renderer - improving sampling, multi-bounce indirect illumination, and overall path tracing fidelity while keeping it tractable for large-scale, time-stepped simulation.
• Own and extend physically-based materials through our NVIDIA MDL integration - microfacet BRDFs, layered and coat materials, and correct energy conservation in OptiX closest-hit evaluation.
• Develop our spectral and radiometric pipeline - wavelength-dependent sky and material response, spectral-to-XYZ-to-RGB conversion, and physically-grounded units from scene radiance through to sensor output.
• Push the atmosphere and volumetrics - the spectral sky model (Prague Sky Model), volumetric clouds (multi-layer, multi-scattering, phase functions, aerial perspective), participating media, and fog.
• Improve camera image formation - the GPU ISP (demosaic, tone mapping, exposure/EV100, auto white balance via correlated color temperature), and physically-based lens and sensor effects such as motion blur, rolling shutter, lens distortion, and sensor noise.
• Design and tune sampling and denoising strategies - importance sampling, blue-noise and stratified sampling, MIS, and spatial/temporal denoising that reduce variance without compromising physical accuracy.
• Profile and optimize the per-frame GPU pipeline - OptiX launches, BVH (GAS/IAS) construction, material uploads, and denoising - to render more geometry and richer materials within budget.
• Contribute to the OpenUSD scene and material pipeline and collaborate with adjacent teams to plumb new lighting, weather, and sensor parameters end-to-end.
• Uphold high standards through technical design documents, code review, reference-image regression testing, and mentorship of other engineers.
  

• BS, MS, or PhD in Computer Science, Computer Graphics, or equivalent experience.
• 8+ years of professional software engineering experience, with a substantial portion focused on real-time or physically-based rendering.
• Deep, hands-on graphics expertise: physically-based rendering, BRDF / microfacet theory, Monte Carlo path tracing, importance sampling, and light transport. You can read a SIGGRAPH paper and turn it into correct, performant shader code.
• Strong C++ skills (modern C++17/20), including performance optimization, memory management, and clean API and system design in a large codebase.
• Significant GPU programming experience - CUDA and/or a ray tracing API (OptiX, DXR, Vulkan RT) - with a real understanding of GPU architecture and execution.
• Solid grounding in the math of rendering: linear algebra, sampling and probability, radiometry, and color science.
• Experience profiling and optimizing GPU rendering workloads (e.g., Nsight) on real, complex scenes.
• Strong communication and collaboration skills, and a track record of driving technical design across teams.
• Movie industry experience background
  

• Experience implementing or extending a production renderer, game engine rendering core, offline/path-tracing renderer, or commercial graphics product.
• Experience with NVIDIA MDL, OSL, or other physically-based material systems.
• Experience with OptiX specifically, or other GPU ray tracing / BVH-based pipelines.
• Spectral rendering, atmospheric scattering models (Prague, Hosek-Wilkie, Bruneton), or volumetric cloud rendering (Schneider/Nubis-style ray marching, Hillaire multi-scattering).
• Camera and sensor imaging: ISP pipelines, sensor noise modeling, lens distortion, motion blur, rolling shutter, exposure and white balance.
• Experience with OpenUSD scene description and asset pipelines.
• Familiarity with ROS and integrating rendering output into robotics / AV data stacks.
• Experience enabling rendering for machine learning workloads - synthetic data generation, reinforcement learning environments, or closed-loop simulation.
• Contributions to open-source rendering, graphics, or simulation projects, or relevant publications.

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