Senior Robotics Applied Scientist

Who you are

• PhD, or Master's degree and 6+ years of applied research experience
• Experience programming in Java, C++, Python or related language
• Research or applied experience in one or more of: tactile sensing, manipulation policy learning, contact-rich manipulation, dexterous grasping, or multi-modal perception for robotics
• Experience deploying learned policies or control algorithms on physical robotic hardware
• Experience leading technical initiatives and key deliverables
• Experience bridging research with practical engineering implementation in physical robotic systems
• Hands-on experience with modern tactile sensing hardware and tactile signal processing
• Strong 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 manipulation
• Experience developing manipulation policies using reinforcement learning, imitation learning, or foundation models for contact-rich tasks
• Demonstrated experience with sim-to-real transfer for tactile-enabled manipulation
• Familiarity with both learned and classical approaches to contact dynamics, including impedance and admittance control
• Experience with force and torque control, and with proprioceptive force estimation on torque- or current-controlled actuators
• Experience with robotics simulation stacks (e.g., Isaac, MuJoCo) and robot modeling (URDF)
• Experience with teleoperation and demonstration-collection pipelines for dexterous hands
• Familiarity with multi-finger or multi-contact gripper platforms

What the job involves

• We are seeking a Senior 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 demonstrate strong working knowledge of modern policy learning methods, with expertise in taking algorithms from simulation onto real hardware
• That means being comfortable working against real physical constraints and treating sim-to-real as a core research problem rather than optimizing for benchmark numbers alone
• 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
• 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
• Your morning might start with a standup alongside hardware and controls engineers reviewing overnight sim-to-real training runs on a multi-finger gripper, debugging why a grasp policy that worked in simulation is slipping on a real sensor array
• Mid-morning you join the weekly tactile sensing workstream to align on sensor integration milestones and share early results from a contact-state estimator you have been prototyping
• After lunch you spend a focused block iterating on a reinforcement learning reward formulation, testing variations in simulation before queuing runs on the cluster
• Later you pair with a mechanical engineer to review sensor placement trade-offs on an upcoming gripper revision, then wrap the day by drafting a short experiment write-up and sketching next steps for a conference submission
• No two days look exactly the same: one week you may be collecting teleoperated demonstrations on the physical robot to seed an imitation learning pipeline; the next you could be deep in a codebase refactor to support a new tactile modality
• Throughout, you balance hands-on research — writing algorithms, running experiments, analyzing data — with cross-team collaboration and mentoring
• The common thread is moving from research insight to working capability on real hardware, with a tight feedback loop between simulation and the physical world