Senior Engineer, Health AI (Medical Imaging & Clinical Decision Support)

Additional Location(s): US-MN-Arden Hills; US-MN-Maple Grove

Diversity - Innovation - Caring - Global Collaboration - Winning Spirit - High Performance

At Boston Scientific, we’ll give you the opportunity to harness all that’s within you by working in teams of diverse and high-performing employees, tackling some of the most important health industry challenges. With access to the latest tools, information and training, we’ll help you in advancing your skills and career. Here, you’ll be supported in progressing – whatever your ambitions.       

About the role:

Boston Scientific ranked #2 among medical device companies on Forbes America’s Best Places to Work for Engineers 2026. Whether your passion lies in systems, software, human factors, or beyond, this is a place where you can grow your career and be part of something bigger—advancing science for life.

At Boston Scientific, you’ll have the opportunity to harness all that’s within you by working in teams of diverse and high-performing employees, tackling some of the most important health industry challenges. With access to the latest tools, information, and training, we’ll help you advance your skills and career—supported in progressing, whatever your ambitions.

The healthcare industry is experiencing rapid digital transformation, reshaping patient experiences and expectations. Boston Scientific sees this evolution as an opportunity to advance science through innovative, insights-driven digital solutions.

As a Senior Engineer, Health AI, you will design, build, validate, and deploy AI and machine learning solutions that support clinical and product outcomes across the enterprise. You will work closely with cross-functional partners across data science, clinical, regulatory, quality, cybersecurity, product, and platform teams to deliver robust, scalable, and compliant AI solutions for physicians and patients. This role emphasizes medical imaging and early-stage product lifecycle activities, from inception through demonstration, and requires a strong AI-first engineering mindset, disciplined MLOps practices, collaborative partnership, and commitment to Responsible AI.

Work model, sponsorship, relocation:

At Boston Scientific, we value collaboration and synergy. This role follows a hybrid work model requiring employees to be in the local office at least three days per week. Boston Scientific will not offer sponsorship or take over sponsorship of an employment visa for this position at this time. 

Your responsibilities will include:

● Engineer end-to-end healthcare AI solutions, including medical imaging algorithms, spanning data acquisition and curation, preprocessing, feature engineering, model development, evaluation, and deployment readiness.

● Demonstrate strong clinical fluency by interpreting clinical concepts, disease-state context, and workflows, and incorporating clinical informatics, labeling protocols, and ground-truth strategies into algorithm design.

● Translate clinical questions into measurable machine learning objectives and clinically meaningful endpoints aligned to intended use, patient impact, and regulatory expectations.

● Design and implement scalable, production-ready system architectures that meet performance, safety, privacy, cybersecurity, and regulatory requirements, including SaMD-aligned design controls where applicable.

● Build reproducible development workflows with versioned datasets, code, experiments, and model artifacts, ensuring traceable lineage from data inputs to model outputs.

● Lead technical execution with internal and external partners across the product lifecycle, setting engineering strategy, solution architecture, delivery plans, and integration approaches to meet clinical and product goals.

● Serve as a technical bridge across Health AI, AI Engineering, Data Science, IT, Enterprise Architecture, Product, Quality, Regulatory, Privacy, and Cybersecurity teams.

● Contribute hands-on in agile delivery by defining epics and stories, estimating work, managing technical dependencies, and driving measurable value delivery while supporting project planning and roadmaps.

● Develop and optimize imaging and multimodal models, including segmentation, detection, classification, and quantification, using modern deep learning techniques and appropriate augmentation, sampling, and calibration methods.

● Define and execute robust evaluation plans encompassing internal validation, cross-site generalization, subgroup performance, robustness, calibration, and failure-mode analysis using clinically relevant metrics.

● Enable strong MLOps practices in partnership with platform teams, including CI/CD, automated testing, reproducible training, deployment, monitoring