(Senior) AI Engineer - Reinforcement Learning Manipulation, RIVR
Amazon
- Location
- Onsite (Zurich, Zurich)
- Employment
- Full-time
- Level
- Senior Level
Posted 2 days ago
About the Role
RIVR, an Amazon company, is building Physical AI by deploying autonomous robots for real-world doorstep delivery. This role involves developing advanced reinforcement learning algorithms to enable robust, contact-rich dexterous manipulation for these autonomous systems.
Skills
Reinforcement Learning
Deep Learning
Robotic Manipulation
C++
Python
Computer Vision
Tactile Sensing
Trajectory Optimization
Grasp Synthesis
Neural Networks
Policy Optimization
Sim-to-real Transfer
Domain Adaptation
Proprioceptive Sensing
Autonomous Systems
Full job details
RIVR, an Amazon company, is building Physical AI by deploying autonomous robots for real-world doorstep delivery. Operating daily in diverse urban environments, RIVR's robots continuously learn from and navigate the millions of scenarios encountered during deliveries. By owning the full stack from software to hardware, RIVR is purpose-built for safety, reliability, and the customer from day one.
Reinforcement learning is transforming our robotic intelligence, enabling autonomous behavior without human guidance. We are seeking a Senior AI Engineer with deep expertise in reinforcement learning and deep learning, including supervised and self-supervised learning with a focus on dexterous manipulation. Your role will involve leveraging both simulated and real-world data to address practical challenges in dynamic grasping, contact-rich manipulation, and object interaction. If you are passionate about advancing AI and developing innovative solutions, join us in shaping the future of intelligent robotics.
Key job responsibilities
Develop cutting-edge reinforcement learning algorithms to enable robust, contact-rich dexterous manipulation, translating vision, depth, tactile, and proprioceptive sensor input into precise end-effector and joint-level motor commands.
Design, test, and refine algorithms to solve complex real-world manipulation challenges, such as handling diverse package form factors, dynamic hand-offs, and operating door handles or latches.
Collaborate with the foundation model team to innovate methods that leverage both simulated and real-world data. Basic Qualifications: - Master’s degree or higher in a relevant field such as Engineering, Robotics, or Machine Learning.
- A minimum of five years of industry or research experience, with PhD experience applicable.
- Strong deep learning fundamentals, including supervised and self-supervised learning techniques, and reinforcement learning, including Markov Decision Processes (MDPs), neural network architectures, policy optimization algorithms, model-based vs. model-free RL, exploration-exploitation strategies, value function methods, transfer learning, domain adaptation, sim-to-real transfer, etc.
- Strong background in robotics including autonomy and/or manipulation.
- Experience with deploying artificial neural networks on hardware platforms.
- Ability to write production-level code in modern C++.
- Ability to prototype algorithms and train deep neural networks in Python.
- Strong background in robotic manipulation, including dynamics, grasp synthesis, and trajectory optimization. Preferred Qualifications: - PhD degree in Robotics, Engineering, Computer Science, Machine Learning or a similar discipline, or an equivalent amount of research experience.
- Publications at top-tier conferences (e.g., ICRA, IROS, CoRL, RSS) specifically focusing on robotic manipulation, grasping, or contact-rich RL.
- Demonstrated experience working with tactile sensing, multi-fingered robotic hands, or bimanual manipulation.
Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
Reinforcement learning is transforming our robotic intelligence, enabling autonomous behavior without human guidance. We are seeking a Senior AI Engineer with deep expertise in reinforcement learning and deep learning, including supervised and self-supervised learning with a focus on dexterous manipulation. Your role will involve leveraging both simulated and real-world data to address practical challenges in dynamic grasping, contact-rich manipulation, and object interaction. If you are passionate about advancing AI and developing innovative solutions, join us in shaping the future of intelligent robotics.
Key job responsibilities
Develop cutting-edge reinforcement learning algorithms to enable robust, contact-rich dexterous manipulation, translating vision, depth, tactile, and proprioceptive sensor input into precise end-effector and joint-level motor commands.
Design, test, and refine algorithms to solve complex real-world manipulation challenges, such as handling diverse package form factors, dynamic hand-offs, and operating door handles or latches.
Collaborate with the foundation model team to innovate methods that leverage both simulated and real-world data. Basic Qualifications: - Master’s degree or higher in a relevant field such as Engineering, Robotics, or Machine Learning.
- A minimum of five years of industry or research experience, with PhD experience applicable.
- Strong deep learning fundamentals, including supervised and self-supervised learning techniques, and reinforcement learning, including Markov Decision Processes (MDPs), neural network architectures, policy optimization algorithms, model-based vs. model-free RL, exploration-exploitation strategies, value function methods, transfer learning, domain adaptation, sim-to-real transfer, etc.
- Strong background in robotics including autonomy and/or manipulation.
- Experience with deploying artificial neural networks on hardware platforms.
- Ability to write production-level code in modern C++.
- Ability to prototype algorithms and train deep neural networks in Python.
- Strong background in robotic manipulation, including dynamics, grasp synthesis, and trajectory optimization. Preferred Qualifications: - PhD degree in Robotics, Engineering, Computer Science, Machine Learning or a similar discipline, or an equivalent amount of research experience.
- Publications at top-tier conferences (e.g., ICRA, IROS, CoRL, RSS) specifically focusing on robotic manipulation, grasping, or contact-rich RL.
- Demonstrated experience working with tactile sensing, multi-fingered robotic hands, or bimanual manipulation.
Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.