Principal Engineer, AI and Machine Learning Software

Murata America•Oakdale, MN

22h•$140,967 - $160,000•Hybrid

About The Position

Murata Vios is looking for a talented and motivated Principal Engineer specializing in AI and Machine Learning (ML). This role combines technical leadership with hands-on development, defining the technical direction for AI/ML technologies. The AI & MI Principal Engineer designs, develops, and deploys innovative algorithms that enhance our medical devices and improve patient outcomes. This position works alongside cross-functional teams to deliver impactful solutions while adhering to regulatory standards.

Requirements

Ph.D. with 7+ years or Master’s degree with 15+ years of relevant industry experience in computer science, Software Engineering, Machine Learning, Biomedical Engineering, or a related field.
Experience developing deep learning models using architectures such as CNNs, RNNs/LSTMs, transformers, and other modern techniques for biomedical signal, time-series, and medical image analysis.
Experience with MLOps practices, including experiment tracking, model versioning, CI/CD for machine learning, automated training pipelines, and monitoring model performance in production.
Experience deploying AI/ML models using cloud platforms and containerized technologies such as AWS, Azure, Docker, Kubernetes, ONNX, or TensorRT.
Knowledge of explainable AI (XAI), model interpretability, AI risk management, and validation practices for regulated healthcare and medical device applications.
Experience in digital signal processing (DSP), signal conditioning, feature extraction, time-series analysis, and statistical signal processing. Experience with wavelet analysis and frequency-domain techniques is preferred.
Proficiency in MATLAB, Linux, Python.
Experience with software engineering best practices, including object-oriented design, code reviews, unit and integration testing, version control (Git), and Agile development methodologies.
Knowledge of FDA regulations, ISO standards (e.g., ISO 13485), and compliance requirements applicable to medical devices.
Excellent problem-solving and communication skills with a strong analytical mindset.

Nice To Haves

Experience with wavelet analysis and frequency-domain techniques is preferred.
Experience with foundation models, large language models (LLMs), retrieval-augmented generation (RAG), or multimodal AI applications is a plus.

Responsibilities

Lead the architecture and design of scalable AI platforms and production of ML systems.
Provide technical leadership and mentorship to AI/ML engineers.
Lead technical reviews and make key architectural decisions for AI-enabled products.
Evaluate and introduce emerging AI technologies that provide competitive advantage.
Partner with product management, clinical teams, quality, regulatory affairs, and software engineering to define long-term AI/ML technology strategy and product roadmaps.
Collaborate with software engineers to integrate AI/ML models into existing software frameworks and ensure seamless operation of medical device systems.
Design and develop machine learning algorithms that analyze medical data (e.g., images, biosignals) to support diagnostic and predictive capabilities.
Design, develop, and deploy scalable AI/ML pipelines from research through production, ensuring reproducibility, reliability, and maintainability.
Develop and optimize deep learning models for biomedical signal, time-series, and medical image analysis.
Evaluate, validate, and optimize models for robustness, interpretability, and regulatory compliance.
Collect, preprocess, and analyze large biomedical datasets, ensuring data integrity and compliance with privacy regulations (e.g., HIPAA).
Optimize existing machine learning models for performance, accuracy, and efficiency; conduct testing and validation according to regulatory standards (e.g., ISO 13485).
Prepare technical documentation such as design specifications, testing protocols, and user manuals in compliance with medical device regulations.
Keep abreast of the latest advancements in AI/ML technologies and best practices as well as regulatory changes pertinent to the medical device industry.
Provide ongoing support and enhancements for deployed models and algorithms based on user feedback and performance monitoring.