About Remedy Robotics

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 now need your help to bring this technology out of the laboratory and into hospitals everywhere.

The Role

We’re looking for someone to continue leveraging our vast trove of medical imaging data in order to train and deploy deep neural network models. These models enable our surgical robot to understand and reason about both our robot and the patient’s anatomy, which ultimately gives doctors the insight and control necessary to quickly and safely complete the procedure.

You Will

• Train deep neural networks to detect salient features in medical imaging data

• Understand and interpret the training data distributions and labels, and their effect on model performance

• Develop metrics that reveal neural network performance, thereby providing actionable information for improving the models

• Deploy trained neural networks on a real-time surgical robotic system

You Have

• 5+ years of machine learning engineering experience

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

• Expertise with Python

• Experience training image-based deep neural networks, including

  • Deep neural network libraries such as Tensorflow or PyTorch

  • Defining training and validation datasets

  • Using data augmentations during training

  • Selecting loss functions and metrics

  • Conducting large-scale experiments to determine actionable improvements

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

Nice to Haves

• Hands-on familiarity with cloud data ingestion pipelines

• Expertise in MLops with large, high-dimensional, and heterogeneous data

• Experience with cloud infrastructure tools

• Expertise with deep neural networks for unsupervised learning, such as VAEs or GANs