**Job Responsibilities** + Ideation, research, prototype, and implement CV and LLM-based features tailored to personal health products . + Maintain hands-on expertise with current CV and LLM frameworks, libraries, and tooling (PyTorch, TensorFlow, Hugging Face, OpenCV, ONNX, etc.). Apply and map product requirements to appropriate AI solutions; evaluate feasibility, performance, latency, and privacy considerations. + Design and run experiments to validate AI concepts with real-world data and consumer feedback. + Work with suppliers, firmware teams, embedded engineers, and UX designers to integrate AI models into edge or cloud-enabled devices. + Ensure data quality, labeling strategy, and ML lifecycle management (training, evaluation, CI/CD for models). + Stay abreast of AI trends, publish internal learnings, and propose novel product concepts and features. + Optimize models for on-device inference (model compression, quantization, pruning) where necessary. + Address regulatory, safety, and privacy requirements for consumer health applications. **Required Qualifications** + Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, AI/ML, or related field. + 3+ years of hands-on experience in computer vision and/or LLMs, ideally applied to consumer products or embedded systems. + Strong programming skills in Python; experience with PyTorch/TensorFlow and ML tooling. + Solid understanding of CV (image processing, CNNs, transformers for vision), and modern LLM concepts (transformers, retrieval-augmented generation, fine-tuning). + Experience with model deployment on edge devices or cloud platforms; knowledge of ONNX, TensorRT, TFLite is a plus. + Experience working with cross-functional teams and external suppliers. Creative and strong problem-solving skills and product mindset—ability to translate consumer needs into AI features. + Good communication skills and ability to explain technical concepts to non-technical stakeholders. + Familiarity with data privacy regulations (GDPR, CCPA) and ethical AI considerations is desirable. **Suggested Additional Requirements (recommended)** + Experience in mobile or embedded AI for consumer devices. + Experience in building pipelines for data labeling, augmentation, and synthetic data generation. + Knowledge of MLOps practices: experiment tracking, model versioning, deployment monitoring. + Prior work with signal processing or sensor data (camera modules, IR sensors) for personal care devices. + Portfolio of projects or GitHub demonstrating practical CV/LLM implementations. **Competencies & Behaviors** + Curiosity & Learning: proactively follows AI research and translates it into applied solutions. + Product Orientation: prioritizes user value and manufacturability when proposing models. + Collaboration: works effectively with engineers, designers, suppliers, and commercial teams. + Pragmatism: balances model performance with latency, power, and cost constraints. + Ownership & Delivery: takes end-to-end responsibility from prototype to integration. **How we work together** We believe that we are better together than apart. For our office-based teams, this means working in-person at least 4 days per week. Onsite roles require full-time presence in the company’s facilities. Field roles are most effectively done outside of the company’s main facilities, generally at the customers’ or suppliers’ locations. Indicate if this role is an office/field/onsite role. **About Philips** We are a health technology company. We built our entire company around the belief that every human matters, and we won't stop until everybody everywhere has access to the quality healthcare that we all deserve. Do the work of your life to help the lives of others. * Learn more about our business. * Discover our rich and exciting history. * Learn more about our purpose. If you’re interested in this role and have many, but not all, of the experiences needed, we encourage you to apply. You may still be the right candidate for this or other opportunities at Philips. Learn more about our culture of impact with care here.