We are looking for a Senior Principal, Algorithms Engineer to join the R&D team developing medical imaging AI solutions focused on segmentation, analysis, and 3D reconstruction to develop medical applications.
This is a handson role with responsibility for building, integrating, and maintaining endtoend ML-driven systems, from data ingestion and model development through inference, deployment, and application integration.
How you will make an impact:
Architecture & Engineering:
Contribute to system architecture and engineering standards across ML, data, and deployment components.
Design and implement end to end ML systems, covering data pipelines, model training, inference services, and application integration.
Participate in design reviews and technical decision making with a strong hands on implementation focus.
Medical Imaging & Machine Learning:
Apply deep understanding of ML development practices, including model architecture design, training strategies, evaluation, and inference optimization.
Optimize models for volumetric medical data with respect to accuracy, memory usage, and runtime performance.
Develop and optimize deep learning models for medical image segmentation.
Data Handling & Preprocessing:
Build and maintain production grade pipelines for large DICOM datasets.
Implement robust preprocessing and augmentation workflows supporting both training and inference at scale.
Integration, Deployment & MLOps:
Integrate ML models into production medical applications and backend services.
Design and implement inference and deployment strategies, including model versioning, performance tuning, and reliability in AWS based environments.
Work closely with other software developers to integrate ML solutions into the broader platform and end user applications.
Quality, Validation & Collaboration:
Work closely with SQA & Automation engineers to design ML systems for testability and to implement advanced validation strategies, including end to end, regression, and performance validations.
Support CI/CD pipelines by enabling automated testing and validation of ML models, data pipelines, and application integrations.
Collaboration & R&D:
Collaborate with engineering, QA, clinicians, and product teams to validate functionality, performance, and usability.
Stay current with advances in modern ML architectures, training techniques, and deployment practices, contributing hands on to R&D initiatives.
This is a handson role with responsibility for building, integrating, and maintaining endtoend ML-driven systems, from data ingestion and model development through inference, deployment, and application integration.
How you will make an impact:
Architecture & Engineering:
Contribute to system architecture and engineering standards across ML, data, and deployment components.
Design and implement end to end ML systems, covering data pipelines, model training, inference services, and application integration.
Participate in design reviews and technical decision making with a strong hands on implementation focus.
Medical Imaging & Machine Learning:
Apply deep understanding of ML development practices, including model architecture design, training strategies, evaluation, and inference optimization.
Optimize models for volumetric medical data with respect to accuracy, memory usage, and runtime performance.
Develop and optimize deep learning models for medical image segmentation.
Data Handling & Preprocessing:
Build and maintain production grade pipelines for large DICOM datasets.
Implement robust preprocessing and augmentation workflows supporting both training and inference at scale.
Integration, Deployment & MLOps:
Integrate ML models into production medical applications and backend services.
Design and implement inference and deployment strategies, including model versioning, performance tuning, and reliability in AWS based environments.
Work closely with other software developers to integrate ML solutions into the broader platform and end user applications.
Quality, Validation & Collaboration:
Work closely with SQA & Automation engineers to design ML systems for testability and to implement advanced validation strategies, including end to end, regression, and performance validations.
Support CI/CD pipelines by enabling automated testing and validation of ML models, data pipelines, and application integrations.
Collaboration & R&D:
Collaborate with engineering, QA, clinicians, and product teams to validate functionality, performance, and usability.
Stay current with advances in modern ML architectures, training techniques, and deployment practices, contributing hands on to R&D initiatives.
Requirements:
MSc. in Computer Science, Software Engineering, Biomedical Engineering, or equivalent practical experience.
8+ years of professional Algorithms and Software Development experience, including hands on ownership of end to end ML systems from design to production.
Deep knowledge and understanding of machine learning development, including modern architectures, training methodologies, inference optimization, and deployment.
Strong Python skills with proven hands n experience using PyTorch.
Proven experience with
U Net variants (3D UNet, nnUNet)
3D CNNs
MONAI
3D Slicer
YOLO variants
and medical image segmentation pipelines.
Solid knowledge of medical imaging data formats (DICOM, NIfTI).
Strong foundation in image processing and computer vision.
Experience optimizing GPU accelerated and performance sensitive ML workloads.
Practical experience with CI/CD pipelines, automated testing, and Git based workflows.
Excellent documentation skills; strong English communication; collaborative and accountable team player.
What else we look for:
Advanced degree (M.Sc./Ph.D.) in a relevant field.
Experience deploying ML systems on AWS (including SageMaker).
Experience working in regulated or medical software environments.
Background in 3D visualization or interactive medical applications.
Full stack experience (Node.js, React, PostgreSQL).
MSc. in Computer Science, Software Engineering, Biomedical Engineering, or equivalent practical experience.
8+ years of professional Algorithms and Software Development experience, including hands on ownership of end to end ML systems from design to production.
Deep knowledge and understanding of machine learning development, including modern architectures, training methodologies, inference optimization, and deployment.
Strong Python skills with proven hands n experience using PyTorch.
Proven experience with
U Net variants (3D UNet, nnUNet)
3D CNNs
MONAI
3D Slicer
YOLO variants
and medical image segmentation pipelines.
Solid knowledge of medical imaging data formats (DICOM, NIfTI).
Strong foundation in image processing and computer vision.
Experience optimizing GPU accelerated and performance sensitive ML workloads.
Practical experience with CI/CD pipelines, automated testing, and Git based workflows.
Excellent documentation skills; strong English communication; collaborative and accountable team player.
What else we look for:
Advanced degree (M.Sc./Ph.D.) in a relevant field.
Experience deploying ML systems on AWS (including SageMaker).
Experience working in regulated or medical software environments.
Background in 3D visualization or interactive medical applications.
Full stack experience (Node.js, React, PostgreSQL).
This position is open to all candidates.
