AI/ML Scientist - Neuroclinical AI

Siemens Healthineers

1d•$132,500 - $182,182

About The Position

Join Siemens Healthineers’ Digital Technology & Innovation (DTI) team in Princeton, New Jersey, a hub for advancing artificial intelligence and digital technologies in healthcare. As an AI/ML Scientist on the Neuroclinical AI team, you will develop AI algorithms and models for image-guided neurovascular interventions, supporting care from procedure planning through real-time navigation in the angiography suite. The primary clinical focus is acute ischemic stroke and intracranial aneurysm treatment, with other neurointerventional procedures also in scope. You will collaborate with clinical partners, imaging scientists, and product development teams to turn interventional workflow needs into robust, real-time AI solutions. Your work will span model development, validation, and deployment in a regulated medical-device environment. The position also involves coordinating project activities, contributing to research and intellectual property, and proposing new AI/ML initiatives in image-guided neurointervention.

Requirements

MS or PhD in Computer Science, Electrical Engineering, Biomedical Engineering, Robotics, or a related field.
2–5 years of experience in AI/ML development in industry or academia.
Hands-on experience with Python and PyTorch or TensorFlow.
Experience developing and validating machine-learning models for medical imaging or computer-vision applications.
Strong problem-solving and analytical thinking skills.
Ability to clearly communicate technical concepts to technical and clinical audiences.

Nice To Haves

Experience with X-ray fluoroscopy, angiography, or cone-beam CT imaging, ideally in neurovascular or other interventional applications.
Experience with real-time object detection, segmentation, or tracking, particularly of thin or low-contrast structures in image sequences.
Experience with 2D/3D registration, multimodal image fusion, or 3D vascular modeling, such as vessel segmentation and centerline extraction.
Experience with large-scale self-supervised pretraining or foundation models and efficient deployment techniques such as knowledge distillation, quantization, and optimized inference using tools such as TensorRT or ONNX.
Experience with simulation, digital twins, or synthetic data generation, such as digitally reconstructed radiographs or physics-based device or blood-flow simulation.
Experience with reinforcement learning, imitation learning, or robotic navigation, especially for autonomous or assisted catheter and guidewire navigation.
Familiarity with stroke and aneurysm procedures such as mechanical thrombectomy, endovascular coiling, and flow diversion.
Publications in venues such as MICCAI, IPCAI, IEEE TMI, Medical Image Analysis, CVPR, or NeurIPS.

Responsibilities

Develop AI algorithms for real-time detection and tracking of interventional devices, including guidewires, microcatheters, stent retrievers, coils, and flow diverters, in live fluoroscopy and angiography sequences.
Develop 2D/3D fusion and registration methods to bring pre-procedural and intra-procedural 3D imaging and planning information into the live 2D angiographic view.
Build foundation models for interventional and neurovascular imaging and distill them into compact, efficient models that meet real-time latency requirements on clinical hardware.
Build interventional digital twins and simulators of neurovascular anatomy, device behavior, and X-ray image formation for synthetic data generation and for training and evaluating device-navigation agents.
Translate clinical requirements for neurointerventional workflows into data, annotation, and model-performance requirements.
Validate and statistically evaluate AI algorithms, including their accuracy, robustness, and latency under realistic procedural conditions.
Coordinate data collection, data annotation, model development, and validation throughout project lifecycles, often across multiple concurrent projects.
Contribute to intellectual property generation and peer-reviewed research with clinical and technical stakeholders.
Propose and drive new AI/ML-based initiatives in image-guided neurointervention.
Keep abreast of developments in the field and maintain knowledge of state-of-the-art technologies and high-performance computing architectures.