Research Engineer, Robotics

Meta•Redmond, WA

1d•$219,000 - $301,000

About The Position

Reality Labs Research (Reality Labs Research) brings together a multidisciplinary and highly interdisciplinary team of researchers and engineers to create the future of dexterous robotic manipulation. We are seeking a senior staff Research Engineer to design and build a custom CUDA-based compute renderer for robotics. You will own this end-to-end — architecting and implementing a novel GPU rendering system that serves as the visual backbone for robot learning at scale. This is a deeply technical, hands-on IC role for someone who has built rendering systems before.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
Bachelor's degree in Computer Science, Computer Engineering, Physics, or Mathematics (or equivalent practical experience)
10+ years of experience in GPU programming and real-time or offline computer graphics
Expert-level CUDA development including kernel optimization, GPU memory hierarchy, and performance tuning
Deep expertise in ray tracing and/or rasterization algorithms and their GPU implementations
Track record of building rendering systems or GPU compute pipelines
Experience with C++ and systems programming, including performance-critical codebases

Nice To Haves

Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
Master's or Ph.D. in Computer Science, Computer Graphics, Physics, or related field
Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
Experience with physically-based rendering, global illumination, or production rendering pipelines
Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
Familiarity with NVIDIA datacenter GPU architectures (Hopper, Blackwell) and how they differ from consumer GPUs for rendering workloads
Knowledge of robotics simulation or physics engines (MuJoCo, PhysX, Isaac Sim)
Experience integrating rendering systems into ML training pipelines (PyTorch, JAX)
Experience building renderers or graphics engines from scratch in a professional setting
Familiarity with OptiX, Vulkan, or custom ray tracing implementations on NVIDIA hardware

Responsibilities

Design and implement a custom compute renderer: Build a CUDA compute renderer supporting rasterization and ray tracing, optimized for high-throughput batch rendering on datacenter GPUs
Write high-performance GPU kernels: Develop and optimize kernels for core rendering operations including geometry processing, shading, light transport, and image synthesis
Produce ML-ready rendering outputs: Generate rendering outputs (RGB, depth, segmentation) suitable for direct consumption by ML training pipelines
Integrate into policy and training pipelines: Embed rendering capabilities into policy training loops, evaluation harnesses, and dataset generation workflows enabling end-to-end visual learning for robotic manipulation
Integrate with physics simulation: Render dynamic scenes including articulated rigid bodies, deformable objects, and skinned meshes in coordination with physics simulation systems
Collaborate on speed/quality tradeoffs: Partner closely with Research Scientists and ML Engineers to understand requirements and make principled tradeoffs between rendering fidelity and throughput
Own the full rendering stack: Maintain end-to-end ownership from scene ingestion through final image output, driving architectural decisions and performance optimization