Boston Scientific · 8 hours ago
Senior Engineer, Health AI (Medical Imaging & Clinical Decision Support)
US-MN-Arden Hills
Full-time
Hybrid
Mid, Senior Level
$89K/yr - $170K/yr
7+ years expBoston Scientific is a leader in medical science, committed to solving significant health challenges through innovative solutions. The Senior Engineer, Health AI will design, build, validate, and deploy AI and machine learning solutions that support clinical and product outcomes, working closely with cross-functional partners across the enterprise.
Health CareMedicalMedical Device
No H1B
Responsibilities
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, and lifecycle management
Deliver scalable inference solutions for imaging and non-imaging use cases, supporting batch and real-time workflows with defined SLAs, observability, and incident response processes
Support validation, quality, and Responsible AI governance through bias and subgroup analysis, robustness testing, calibration, and real-world performance evaluation
Produce engineering documentation and objective evidence aligned to Responsible AI and SaMD governance, supporting design controls, traceability, and audit readiness
Champion privacy-by-design and secure engineering practices, with strong attention to PHI protection, data handling, and clinical safety considerations
Drive continuous improvement and innovation by staying current on healthcare AI and modern machine learning methods and translating advances into governed, scalable product capabilities
Build reusable assets, including model templates, evaluation harnesses, data validation checks, and reference pipelines, to accelerate delivery and improve consistency across programs
Qualification
Required
Bachelor's degree in computer science, engineering, data science, biomedical engineering, or a related technical field, or equivalent practical experience
Minimum of 7 years' experience in software engineering, machine learning engineering, or applied AI
Minimum of 3 years' experience delivering machine learning-enabled products into production environments
Demonstrated experience developing AI and machine learning solutions for healthcare, with a strong emphasis on medical imaging and or multimodal clinical data
Clinical domain fluency, including the ability to understand disease-state context, clinical workflows, and translate clinical needs into algorithm requirements and validation plans
Hands-on experience working with healthcare data such as DICOM imaging, radiology reports, EHR, HL7, FHIR, pathology, waveforms, or claims data, including associated privacy, security, labeling, and data quality considerations
Strong programming skills in Python and experience with deep learning and machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn
Production engineering experience building and operating machine learning-enabled services, including APIs or batch pipelines, containerization, CI/CD, and scalable deployment patterns
Experience with model lifecycle management, including dataset and artifact versioning, performance and drift monitoring, and reliable retraining and release processes
Experience working within regulated product development environments, including SaMD, design controls, documentation, traceability, and risk management, in partnership with Quality and Regulatory teams
Strong communication and documentation skills, with the ability to align technical and non-technical stakeholders on requirements, design tradeoffs, risks, and validation evidence
Preferred
Direct experience delivering AI or machine learning as part of medical device or SaMD programs, including contribution to regulatory submissions or audit-ready documentation
Familiarity with applicable standards and practices for regulated medical software and clinical evaluation, such as ISO 13485, ISO 14971, IEC 62304, IEC 62366, and GMLP
Experience applying Responsible AI principles in healthcare, including bias and subgroup analysis, interpretability, uncertainty, and human-in-the-loop workflows
Strong background in clinical informatics and workflow integration, including radiology or PACS integration and downstream clinical workflow impact
Experience with cloud-native machine learning and MLOps platforms, including Kubernetes, MLflow, feature stores, model registries, and monitoring and observability tools
Familiarity with generative AI patterns applied to healthcare, including LLMs, retrieval-augmented generation, and structured extraction or summarization, with evaluation approaches suitable for regulated environments
Company
Boston Scientific
Boston Scientific is a medical technology company that designs and develops medical devices to diagnose and treat a wide range of condition.
10001+ employees
Funding
Current Stage
Public CompanyTotal Funding
$10.02B2025-02-21Post Ipo Debt· $1.58B
2024-02-22Post Ipo Debt· $2.17B
2022-03-04Post Ipo Debt· $3.28B
Leadership Team
Michael Mahoney
Chairman, President & CEO
Daniel J. Brennan
Chief Financial Officer
Recent News
2026-02-04
2026-02-03
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