Machine Learning Scientist

Applied ScientistAmazon is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building robotic systems that combine frontier AI, sophisticated control, and advanced mechanical design to create adaptable automation capable of working safely alongside humans in dynamic environments. This is an opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic manipulation, locomotion, and human-robot interaction. We are seeking an Applied Scientist to develop tactile
- and force-driven manipulation policies for highly dexterous, multi-fingered robotic hands and grippers. In this role you will research and build policy learning algorithms that enable robotic hands to perform grasping and in-hand manipulation in unstructured, real-world environments, using touch and force feedback as first-class signals rather than afterthoughts. Much of this work is grounded in reinforcement learning. To be successful you need to be highly motivated, dive deep, and deliver to the highest standards. You will work across tactile signal processing, contact-rich manipulation, sim-to-real transfer, and multi-modal sensor fusion to build robust solutions for autonomous grasping, dexterous re-orientation, and fine motor control. You will bring the desire to learn from new challenges, and the problem-solving and communication skills to work within a highly interactive and experienced team.
Key job

responsibilities:
Design and train manipulation policies that leverage tactile and force feedback for dexterous grasping, in-hand manipulation, and contact-rich tasks, using reinforcement learning, imitation learning, or hybrid approaches.
Drive sim-to-real transfer, closing the gap between simulated and physical performance on real robotic hands.
Shape objectives and control strategies with the hardware in mind, accounting for how motors and actuators actually behave: transmission ratios, torque versus power, backdrivability, and thermal limits.
Build simulation-based and on-robot evaluation frameworks, with benchmarks and metrics that make tactile perception and policy performance systematically comparable across iterations.
Own scientific and technical projects within the tactile sensing and manipulation workstreams, driving from research concept through deployment on physical robotic systems — and, at the senior level, set direction across a workstream.
Collaborate with hardware, mechanical design, firmware, and controls teams — and with partner applied science organizations — to translate research advances into deployable robotic capabilities, and to inform sensor and actuator design decisions with policy-level requirements.
Publish at top-tier venues and build collaborations with the external research community.
Contribute to a strong scientific bar on the team through code and design review, and mentor engineers and interns working on real-world manipulation and sensing problems.
Basic

Qualifications:
Experience programming in Java, C++, Python or related languageResearch or applied experience in one or more of: tactile sensing, manipulation policy learning, contact-rich manipulation, dexterous grasping, or multi-modal perception for roboticsExperience deploying learned policies or control algorithms on physical robotic hardwarePreferred

Qualifications:
Experience leading technical initiatives and key deliverablesExperience bridging research with practical engineering implementation in physical robotic systemsHands-on experience with modern tactile sensing hardware and tactile signal processingStrong publication record at major robotics/ML venues (e.g., RSS, CoRL, ICRA, IROS, NeurIPS, ICML, ICLR), including impactful first-author work in tactile sensing or dexterous manipulationExperience developing manipulation policies using reinforcement learning, imitation learning, or foundation models for contact-rich tasksDemonstrated experience with sim-to-real transfer for tactile-enabled manipulationFamiliarity with both learned and classical approaches to contact dynamics, including impedance and admittance controlExperience with force and torque control, and with proprioceptive force estimation on torque
- or current-controlled actuatorsExperience with robotics simulation stacks (e.g., Isaac, MuJoCo) and robot modeling (URDF)Experience with teleoperation and demonstration-collection pipelines for dexterous handsFamiliarity with multi-finger or multi-contact gripper platformsAmazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively