Staff AI Research Engineer

Lab Summary:

Our mission is to empower people to live healthier lives by leveraging our wearables, smartphones, medical devices, AI, and health services.  We research, develop, and commercialize innovative digital health products and solutions by bringing together the full capabilities of our team, including product strategy and management, clinical expertise, partnerships, UI/UX design, usability research, clinical research and validation, software development, AI / Gen AI, and regulatory. Millions of Samsung users’ lives around the world have been touched by our products, including the Samsung HeartWise for cardiac rehabilitation and the Samsung ECG Monitor App. Samsung’s unique advantage in the consumer electronics market and growing focus on Digital Health and AI / Gen AI provides exciting technical challenges and rewarding career experience.  Join us! 

Position Summary:

Samsung Research America Digital Health Team is looking for an outstanding Ph.D.-level Senior AI Research Engineer with solid ML/AI technology background and digital health-related research project experiences in digital biomarker, physiological signal processing and deep learning. You need to have a strong track-record of success in academic and/or industrial settings, with the ability to scale algorithms and models to large, real-world datasets.

You will play a key role in delivering innovation in digital health domain, creating technologies and intellectual properties, deploying and validating novel algorithms, and transferring mature outcomes to production and scientific publications in top-tier venues. We expect candidates who have solid data analytics skills and project experiences in areas such as health foundation models pre and post training, wearable computing, and algorithm development for health applications. Your work will significantly benefit real-world wearable users, seniors, physicians and care givers. Samsung's unique advantage in the consumer electronics market and growing focus on digital health will provide you with exciting technical challenges and a rewarding career experience.

Position Responsibilities:

• Research and develop novel AI/ML algorithms and foundation models for physiological signal processing, sensor fusion, and digital biomarkers in wearable health applications, leveraging self-supervised learning, transformer models, and computer vision models on very large-scale real-world user datasets
• Design and implement deep learning and statistical models to analyze multi-modal biosignals (e.g., EEG, ECG, PPG, motion data) in real-time and batch settings
• Scale algorithms to handle large, multi-terabyte datasets from wearables and biomedical sensors on cloud platforms (AWS, GCP, Azure) while maintaining algorithmic efficiency
• Collaborate with hardware and software engineers to integrate AI models into wearable platforms, ensuring efficient on-device inference
• Conduct feasibility studies, benchmark AI-driven features, and refine models for accuracy and scalability across diverse populations
• Work with clinical and regulatory teams to ensure model robustness, validation, and compliance with healthcare standards
• Stay up-to-date with the latest advancements in digital health, computational neuroscience, and edge AI to drive innovation in wearables
• Generate patents and scientific research papers for top-tier publications

Required Skills:

• PhD in AI, Machine Learning, Biomedical Engineering, Computer Science, or a related field or equivalent combination of education, training, and experience
• 3+ years of experience in data analytics and project experiences in areas such as health foundation models pre and post training, wearable computing, and algorithm development for health applications
• 3+ years of experience in ML/DL frameworks (TensorFlow, PyTorch) and programming languages (Python, C++)
• Track record of publishing in high-impact AI/ML venues (ICML, ICLR, NeurIPS, etc.) or top-tier journals (Nature, Science, etc.)
• Experience with deep learning on time-series data and sequence models (LSTMs, attention mechanisms, temporal convolutions)
• Experience with time-series analysis, signal processing, and sensor fusion for wearable or biomedical applications
• Familiarity with biomedical sensing modalities (EEG, ECG, PPG, SpO2, etc.) and data-driven health insights
• Experience designing and optimizing algorithms to work efficiently with large-scale datasets and distributed computing environments
• Experience in developing PoCs and exploratory research projects in emerging sensing technologies

Special Attributes:

• Understanding of clinical workflows and healthcare data privacy considerations (HIPAA, FDA, MDR, etc.)
• Knowledge of wearable hardware constraints, power optimization, and real-time processing
• Familiarity with on-device ML optimization and model compression techniques
• Experience scaling algorithms and model training on cloud compute infrastructure (AWS, GCP, Azure) or distributed computing environments