QA Engineer: Nyx Renderer & Keystone AI World

About Keystone AI AI
Keystone AI AI builds the simulation platform powering the next generation of robotics and embodied AI. Our flagship product, Keystone AI World, is a unified multi-physics simulation engine paired with Nyx, our in-house GPU-accelerated, path-traced photorealistic renderer. Together they provide a platform for training robotics and embodied AI at scale; letting teams simulate complex physical worlds with high fidelity and speed, from a laptop to datacenter-grade GPUs, and close the gap between simulation and the real world.
About the Role
We're hiring a Senior QA Engineer to own quality across Nyx and the Keystone AI World engines. You'll split your time evenly between hands-on exploratory testing and enhancing our automated test infrastructure. Because both systems run on GPUs and underpin robotics training and scientific workloads, correctness goes beyond "does it run": it's about visual fidelity, numerical accuracy, determinism, and reproducibility across hardware.
We're especially looking for someone with strong knowledge of 3D environments, assets, and rendering, who can reason about what "visually and physically correct" actually means.
What You'll Do
– Maintain and evolve our automated test suites for the Nyx renderer and the Keystone AI World physics engine.
– Develop image-comparison / perceptual-diff tooling to catch rendering regressions, and validation harnesses to catch simulation drift.
– Validate numerical correctness, determinism, and reproducibility across GPUs, drivers, and CUDA versions.
– Perform exploratory and manual testing on 3D scenes, assets, and complex multi-physics scenarios that are hard to automate.
– Set up quality gates in CI for prebuilt wheels across platforms (Linux, Windows, Python 3.10–3.13).
– Triage, reproduce, and clearly document defects; partner with engineers on root cause.

What We're Looking For
– 5+ years in software QA, with strong experience in both test automation and exploratory testing.
– Solid 3D environment and asset knowledge, with familiarity with rendering and simulation/physics.
– Strong Python and C++ skills; comfortable reading performance-sensitive code.
– Understanding of testing challenges in GPU and floating-point systems; non-determinism, tolerance-based assertions, hardware/driver variance.
– Experience building test infrastructure in CI/CD and managing cross-platform builds.
– Excellent debugging instincts and clear, reproducible bug reports.

Nice to Have
– Experience with GPU programming, or ray/path tracing concepts.
– Background in computer graphics (PBR, color management, perceptual image metrics).
– Familiarity with physics or scientific simulation.
– Robotics or sim-to-real experience.