Lead AI/ML Engineer, OCT Medical Imaging

Abbott; Santa Clara (CA); On-site; USD 149,000 - 297,000; Full time

Job summary

Abbott Vascular seeks a Principal AI/ML Engineer to advance OCT-based intravascular imaging with scalable, clinically relevant algorithms. You will own end-to-end ML lifecycle from data prep to deployment in regulated medical devices, collaborating across clinical, imaging, and software teams.

Focus areas include semantic segmentation, image analysis, and real-time inference optimization, leveraging cutting-edge ideas such as foundation models and self-supervised learning while ensuring design

Qualifications

• Masters or PhD in a related field with AI/ML focus.
• 8 years in ML/DL or 4 years with PhD, in industry.
• Strong experience with image analysis: segmentation, detection, classification.
• Proficiency in Python and ML frameworks (PyTorch, TensorFlow).
• Ability to translate research to production-grade software.

Responsibilities

• Design, develop, and deploy ML/DL models for OCT image analysis.
• Lead semantic segmentation algorithm development for OCT vascular imaging.
• Own end-to-end ML lifecycle from data prep to deployment.
• Collaborate with clinical and V&V teams on metrics and validation.
• Develop robust algorithms for diverse patient data and imaging conditions.
• Optimize models for real-time or near real-time inference.
• Contribute to software architecture and design controls documentation.

Skills

Deep learning architectures

Vision transformers

Python programming

Model validation

Experiment design

Cross-functional collaboration

Education

Master’s or PhD in Computer Science, Electrical Engineering, Biomedical Engineering, or related field with AI/ML focus

Tools

PyTorch

TensorFlow

ONNX

TensorRT

TorchScript

Job description

Abbott Vascular seeks a Principal AI/ML Engineer to advance OCT-based intravascular imaging with scalable, clinically relevant algorithms. You will own end-to-end ML lifecycle from data prep to deployment in regulated medical devices, collaborating across clinical, imaging, and software teams.

Focus areas include semantic segmentation, image analysis, and real-time inference optimization, leveraging cutting-edge ideas such as foundation models and self-supervised learning while ensuring design