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Member of Technical Staff - Simulation (Synthetic Data Generation), Frontier AI & Robotics (FAR)

External
Amazon.com Services LLC logoAmazon.com · San Francisco, CA
Full-timeOn-site1w ago
PythonAWSiOS
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About the role

At Frontier AI & Robotics (FAR), we're not just advancing robotics - we're reimagining it from the ground up. Our team is building the future of intelligent robotics through frontier foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's massive computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence - from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.

Requirements

  • Bachelor's degree in computer science or equivalent
  • 5+ years of non-internship professional software development experience
  • 5+ years of programming with at least one software programming language experience
  • 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience
  • Experience as a mentor, tech lead or leading an engineering team
  • Proficiency in Python, familiarity with C++.
  • Software engineering experience working within 3D domains (gaming, visual effects, core engine development, or rendering), and understanding of 3D graphics pipelines, computational geometry, and spatial transformations.
  • Experience building automation or asset pipelines for 3D content creation.
  • Expertise in scalable 3D ecosystem formats (Universal Scene Description (USD), OpenVDB).
  • Experience with 3D content creation and procedural tools (e.g., Blender, Houdini, Unreal Engine, Unity).
  • Background in procedural generation, technical art, or environment generation.
  • Familiarity with rendering technologies (ray tracing, rasterization, physically based rendering).
  • Basic understanding of physical properties in 3D engines (collisions, basic physics solvers) as a nice-to-have.
  • Master's degree in Computer Science, Computer Graphics, or a related field.
  • Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
  • Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
  • Our inclusive culture empowers Amazonians to deliver t

Additional Information

We are seeking a Simulation Engineer to join our AI robotics research team, focusing on high-fidelity synthetic data generation. In this role, you will leverage classic game engine architecture, 3D pipelines, and procedural generation techniques to build highly realistic, scalable environments. You will work alongside researchers to generate the 3D datasets required to train large-scale foundation models for robotics perception. Key job responsibilities - Architect and develop scalable 3D pipelines for high-fidelity synthetic data generation. - Build procedural generation systems to create diverse, dynamic environments and varying object configurations at scale. - Develop and maintain automated asset toolchains that support industry-standard ecosystem formats (e.g., USD, OpenVDB). - Optimize rendering performance and pipeline throughput to meet the massive data requirements of ML training. - Collaborate with researchers to define synthetic data requirements and ensure the generated data meets the visual fidelity needed for sim-to-real transfer.


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