Member of Technical Staff - Simulation, Frontier AI Robotics
Posted 1 day ago
Job Description
We are seeking a Member of Technical Staff Simulation Engineer to join our AI robotics research team developing foundation models for robotics. You will rapidly develop 3D physics-based and photorealistic simulations alongside scientists to enable training large-scale machine learning models.
Key job responsibilities
- Develop simulations for reinforcement learning, closed-loop simulations and synthetic data generation
- Implement essential robotics features, including accurate modeling of sensors, actuators, and controllers
- Build real-to-sim workflows for dynamic environments and robotics tasks
- Implement simulation features to minimize sim-to-real gaps through domain randomization and system identification
- Create asset toolchains supporting industry-standard formats (URDF, MJCF, USD)
- Collaborate closely with a team of ML researchers to enable large-scale robotics training pipelines
About the team
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.
Key job responsibilities
- Develop simulations for reinforcement learning, closed-loop simulations and synthetic data generation
- Implement essential robotics features, including accurate modeling of sensors, actuators, and controllers
- Build real-to-sim workflows for dynamic environments and robotics tasks
- Implement simulation features to minimize sim-to-real gaps through domain randomization and system identification
- Create asset toolchains supporting industry-standard formats (URDF, MJCF, USD)
- Collaborate closely with a team of ML researchers to enable large-scale robotics training pipelines
About the team
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.
Apply for this position
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Amazon
170 jobs posted
About the job
Posted on
May 22, 2026
Apply before
Jun 21, 2026
Job typeFull-time
Location
US, CA
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