Staff Graphics Engineer - 4D Reconstruction & GPU Rendering

Avala

San Francisco (CA)

Hybrid

USD 180,000 - 250,000

Full time

13 hours ago
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Job summary

Avala in San Francisco is seeking a senior architect to lead the 4D reconstruction pipeline, fusing LiDAR, camera, radar, and IMU data into physics-grade ground truth. You will own the Gaussian Splatting renderer, temporal tracking, and scalable compute infrastructure.

You'll guide the rendering team, mentor engineers, and collaborate with ML engineers on auto-labeling and scene understanding models to push autonomous systems forward.

Qualifications

  • 7+ years in graphics, 3D vision, or high-performance computing.
  • Deep expertise in GPU programming (WebGPU, CUDA, Vulkan, or Metal).
  • Strong C++, Rust, or Dart performance optimization experience.
  • Published work or production systems in 3D reconstruction or novel view synthesis.
  • Experience leading technical teams of 3+ engineers.

Responsibilities

  • Lead development of the 4D reconstruction pipeline (Gaussian Splatting, NeRF, multi-view stereo).
  • Design GPU-accelerated compute shaders for real-time point cloud processing and scene rendering.
  • Build temporal tracking and object persistence systems across sensor frames.
  • Architect distributed processing pipelines that scale to fleet-level data volumes.
  • Set technical direction for the rendering team and mentor senior engineers.
  • Collaborate with ML engineers on auto-labeling and scene understanding models.

Skills

Graphics 3D experience
GPU programming
C++ optimization
Rust optimization
Dart optimization
Leadership

Tools

WebGPU
CUDA
Vulkan
Metal

Job description

Avala in San Francisco is seeking a senior architect to lead the 4D reconstruction pipeline, fusing LiDAR, camera, radar, and IMU data into physics-grade ground truth. You will own the Gaussian Splatting renderer, temporal tracking, and scalable compute infrastructure.

You'll guide the rendering team, mentor engineers, and collaborate with ML engineers on auto-labeling and scene understanding models to push autonomous systems forward.

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