About this role
Summary: Research and develop reinforcement learning methods for robotic manipulation, designing and deploying machine learning solutions that enable robots to interact with the physical world. The role involves tackling complex problems, collaborating with engineering teams, and contributing to the scientific roadmap.
Are you passionate about pushing the boundaries of what robots can do? Amazon is looking for a creative and driven Applied Scientist to help build intelligent robotic manipulation systems powered by reinforcement learning. In this role, you will research, design, and deploy machine learning solutions that enable robots to interact with the physical world in increasingly capable ways. You will tackle complex, ambiguous problems where textbook answers don't exist, working backwards from real-world needs to invent new approaches. Your work will directly influence how automation systems learn, adapt, and perform at Amazon's scale — and you will see your research come to life on real hardware.
Key job responsibilities
- Research and develop reinforcement learning methods for robotic manipulation, advancing the state of the art in sim-to-real transfer, contact-rich control, and adaptive grasping strategies.
- Design, implement, and evaluate machine learning models and algorithms, leveraging deep learning frameworks such as PyTorch or TensorFlow to train policies that generalize across varied manipulation tasks.
- Build and maintain production-quality code that integrates scientific prototypes into larger robotics systems, making thoughtful tradeoffs between complexity, performance, and maintainability.
- Collaborate with cross-functional engineering and science teams to define simulation requirements, validate model performance, and iterate on solutions informed by real-world hardware feedback.
- Contribute to the team's scientific roadmap by identifying new research directions, mentoring fellow scientists, and publishing results at peer-reviewed venues when appropriate.
A day in the life
You might start your morning reviewing overnight training runs, analyzing reward curves and failure modes from your latest reinforcement learning experiments. After a quick sync with robotics engineers, you shift to refining a simulation environment to close a gap you spotted between sim and real performance. In the afternoon, you pair with a teammate to design a new evaluation framework, then wrap up by drafting a short write-up of your findings to share with the broader team.
About the team
Our team sits at the intersection of machine learning research and real-world robotics, focused on teaching robots to manipulate objects with precision and adaptability. We combine reinforcement learning, physics-based simulation, and hands-on hardware validation to deliver solutions that work beyond the lab. We value intellectual curiosity, collaborative problem-solving, and a shared commitment to turning ambitious research ideas into tangible impact. If you are excited about building intelligent systems that learn from experience and operate at scale, this is the team for you.
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.
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