Machine Learning Engineer

Cardiovascular disease is the #1 cause of morbidity and mortality in the world. Much of this could be prevented with better access to specialist care. Take stroke as an example: any delay in treatment can lead to permanent disability or death. However, due to a lack of specialist surgeons, the most effective intervention can only be performed in 2% of US hospitals. For patients who present to one of the 98% of hospitals that do not offer the surgery, treatment is either significantly delayed or not offered at all because timely transfer is not feasible.

Our mission is to bring state-of-the-art vascular intervention to anyone, anytime, regardless of their location. Our team of medical clinicians, roboticists, and machine learning experts are working to bridge this gap by building the world’s first remotely-operated, semi-autonomous endovascular surgical robot.

We’ve already done what nobody else could—using our system, doctors from around the world were able to remotely perform this procedure from as far as 8000 miles away. We have now successfully performed first-in-human cases, including a remotely operated procedure, demonstrating the potential of our technology to revolutionize access to life-saving interventions. We now need your help to bring this technology out of the laboratory and into hospitals everywhere.

We’re looking for an interdisciplinary engineer who 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’ll leverage large-scale simulated and real-world datasets to train and evaluate deep learning models that enable robots to understand anatomy, reason about intervention strategies, and safely operate in highly constrained environments.

You will collaborate closely with roboticists, machine learning engineers, clinicians, and hardware teams to rapidly prototype, test, and deploy new capabilities. The ideal candidate is excited by hard, open-ended technical problems and is comfortable moving fluidly between research and production engineering.