R
Remedyroboticsvia Ashby
Autonomy Engineer
San Francisco (hybrid)Posted 2d ago
OtherMid LevelFull-time
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About the Role
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 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.
THE ROLE
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.
Your work will directly contribute to building autonomous systems capable of delivering life-saving interventions when and where human specialists are unavailable.
YOU HAVE
- One of
- Bachelor’s degree and 4+ years experience
- Master’s degree and 2+ years experience
- PhD and 0+ years 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 robotics
- software, such as ROS
- algorithms, such as motion planning
- math, such as transforms
- 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
NICE TO HAVES
- Experience with medical imaging data such as x-rays, CTs, and MRIs
- Experience bridging the sim-to-real gap
- Experience with reinforcement learning
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.
THE ROLE
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.
Your work will directly contribute to building autonomous systems capable of delivering life-saving interventions when and where human specialists are unavailable.
YOU HAVE
- One of
- Bachelor’s degree and 4+ years experience
- Master’s degree and 2+ years experience
- PhD and 0+ years 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 robotics
- software, such as ROS
- algorithms, such as motion planning
- math, such as transforms
- 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
NICE TO HAVES
- Experience with medical imaging data such as x-rays, CTs, and MRIs
- Experience bridging the sim-to-real gap
- Experience with reinforcement learning
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