Research Engineer, Robotics

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 s is a deeply technical, hands-on IC role for someone who has built rendering systems before.

Minimum Qualifications

* 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

Preferred Qualifications

* 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 (Mu Jo Co , 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

About Meta

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