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 are looking to hire an Autonomy Engineer for our Machine Learning Team. What You’ll Do: Develop and deploy autonomy capabilities at the intersection of robotics, machine learning, simulation, and medical imaging Work across the full autonomy stack, including perception, scene understanding, planning, control, and deployment on robotic systems Leverage large-scale simulated and real-world datasets to train, evaluate, and optimize deep learning models Build machine learning systems that enable robots to interpret anatomy, reason through intervention strategies, and operate safely in constrained clinical environments Collaborate cross-functionally with roboticists, machine learning engineers, clinicians, and hardware teams to rapidly prototype and deploy new technologies Design, test, and iterate on advanced robotic capabilities from research through production implementation Solve complex, open-ended technical challenges while balancing research innovation with production-ready engineering Contribute directly to the development of autonomous systems capable of delivering life-saving medical interventions in environments where specialist access is limited Knowledge, Skills, Abilities: Strong proficiency in Python with experience building production-quality software systems Hands-on experience training image-based deep learning models, including: Proficiency with deep learning frameworks such as PyTorch Designing and curating high-quality training and validation datasets Applying data augmentation techniques to improve model robustness Selecting appropriate loss functions and evaluation metrics aligned to task objectives Experience with cloud-based data pipelines and distributed training workflows Running large-scale experiments to evaluate performance and drive model improvements Experience in robotics systems, including: Use of robotics software frameworks such as ROS Familiarity with core motion planning algorithms and robotics fundamentals Understanding of spatial mathematics, including coordinate frames and rigid-body transforms Experience working with robotic simulation environments such as MuJoCo or Isaac Sim Demonstrated ability to design, implement, test, and deploy high-quality software systems Strong ability to learn quickly, iterate rapidly, and collaborate effectively in fast-paced, cross-functional teams Minimum Qualifications: One of the following: Bachelor’s degree with 4+ years of relevant industry experience Master’s degree with 2+ years of relevant industry experience PhD with 0+ years of industry experience Preferred Qualifications: Experience working with medical imaging modalities, including X-rays, CT scans, and MRI data Experience addressing the sim-to-real transfer gap in machine learning or robotics applications Experience with reinforcement learning methodologies and applications