Autonomy Engineer

Remedy Robotics is a medical technology company developing robotic systems for endovascular intervention. Its proprietary technology combines robotics, machine learning, and advanced computer vision to help physicians perform highly precise endovascular procedures and expand access to life-saving stroke and cardiovascular care. Initially focused on neurovascular intervention, Remedy is addressing the limited availability of specialized treatment for time-critical cardiovascular emergencies, with the long-term goal of enabling expert intervention regardless of patient location. Headquartered in San Francisco, Remedy is backed by DCVC, Blackbird, and Tony Fadell's Build Collective, among others.

We’re looking for an interdisciplinary engineer and researcher to lead the development of autonomy and intelligence for the DARPA MASH (Medics Autonomously Stopping Hemorrhage) program. The goal of MASH is to enable autonomous robotic systems to rapidly control internal bleeding in environments where immediate access to expert surgical care is impossible.

This role sits at the intersection of robotics, machine learning, simulation, and medical imaging. You will work across the full stack of autonomy—from perception and scene understanding to planning, control, and deployment on real robotic systems. You will leverage large-scale datasets to train and evaluate deep learning models that enable robots to understand anatomy, reason about intervention strategies, and safely operate a robot in highly constrained human vasculatures. This role is primarily focused on machine learning, but is interdisciplinary and will also involve simulation, medical imaging, and robotics.

You will collaborate closely with other machine learning engineers, roboticists, and clinicians to rapidly prototype, test, and deploy. The ideal candidate is excited by challenging, open-ended technical problems and highly motivated by our ultimate mission: to save lives.

• One of

  • Bachelor’s degree with 4+ years of relevant industry experience

  • Master’s degree with 2+ years of relevant industry experience

  • PhD and 0+ years of industry experience

• Expertise with Python

• Experience training image-based deep neural networks, including

  • Deep neural network libraries such as PyTorch

  • Defining training and validation datasets

  • Using data augmentations during training

  • Selecting loss functions and metrics

  • Cloud-based data and training

  • Conducting large-scale experiments to determine actionable improvements

• Experience with simulators, such as MuJoCo or Isaac

• Experience developing high-quality software, ranging from design and implementation to testing and deployment

• Eagerness to learn on the job, iterate fast, and collaborate

• Experience with robotics

  • software, such as ROS2

  • algorithms, such as motion planning

  • math, such as transforms

• Experience with medical imaging data such as x-rays, CTs, and MRIs

• Experience bridging the sim-to-real gap

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