Sr. Machine Learning Engineer

Neosoft LLC•City of Pewaukee, WI

About The Position

Position Summary: The Senior Machine Learning Engineer is responsible for designing, developing, validating, and deploying advanced machine learning and deep learning models to support NeoSoft’s cardiac MRI analysis platform. This role works closely with software engineers, data scientists, clinicians, and regulatory teams to build robust, reliable, and explainable ML systems suitable for clinical environments and medical device regulatory requirements.

Requirements

Master’s or PhD in Computer Science, Electrical Engineering, Biomedical Engineering, or a related field.
Five or more years of experience developing machine learning or deep learning models in production environments.
Expertise in Python, PyTorch, and medical imaging libraries such as MONAI, SimpleITK, NiBabel, or TorchIO.
Strong background in convolutional neural networks, vision transformers, 3D imaging models, and statistical evaluation methods.
Familiarity with regulatory expectations for machine learning in medical devices, including FDA Good Machine Learning Practice and SaMD guidelines.

Responsibilities

Develop state-of-the-art ML/DL models for segmentation, quantification, classification, and image-based predictions using cardiac MRI data.
Conduct research experiments, evaluate new model architectures, and optimize algorithms for performance, speed, and robustness.
Prepare datasets, including preprocessing, augmentation, and quality control of medical imaging data.
Explore and implement foundation-model techniques such as self-supervised learning and transfer learning.
Convert research prototypes into production-ready models following software engineering best practices.
Optimize models for GPU inference, scalability, and low-latency processing.
Implement model serving pipelines, APIs, continuous integration/continuous deployment, and performance monitoring.
Collaborate with DevOps and engineering teams on integration with suiteHEART and cloud or on-premises infrastructure.
Contribute to documentation required for software as a medical device, including AI design documents, risk analyses, verification and validation plans, and model change protocols.
Ensure ML pipeline traceability, reproducibility, and proper dataset versioning.
Support regulatory submissions with technical justifications and performance reporting.
Partner with product management to translate clinical requirements into ML features.
Provide mentorship to junior ML engineers and interns.