Research Engineer, Robotics

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

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Salary

$219K-$301K not verified - source not recorded; timestamp not recorded

401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

132% above the BLS role benchmark for data and ml aggregate.

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Research Engineer, Robotics Redmond, WA 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. 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,

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