Principal AI Lead - Surgical AI

Johnson & Johnson Med Tech is focused on shaping the future of digital surgery and expanding its robotics and digital solutions offerings across the entire portfolio, with multi-specialty, end‑to‑end solutions in endoluminal intervention, general surgery and a comprehensive digital portfolio. This includes the Polyphonic® platform, a first‑of‑its‑kind digital platform that advances patient empowerment, surgical performance, operating room (OR) collaboration, and operational efficiency.

We are recruiting for a Principal AI Lead within the Polyphonic® Applied AI and ML team located in Santa Clara, CA
. J&J is building the next generation intelligence infrastructure and platforms to capture, extract, and apply intelligence from the OR and surgical workflows and this role will play a key role in shaping and defining core workflows for developing and deploying surgical AI models and agents for the clinical consumption.

This is an opportunity to work with a multi‑functional high‑value team of product managers, applied scientists, software engineers, and clinical experts who will push the boundaries for surgical AI and make it widely accessible. The candidate will own and drive technical direction across the full ML lifecycle—from research and model development to scalable deployment, bringing both deep technical expertise and strong product acuity to build the right platform and user experiences for data scientists and developers, while mentoring engineers and partnering closely with product, engineering, and clinical domain experts.

• AI Development Standardization
  • Conceptualize structured pipelines (ingestion to production), planning flows, agentic workflows
  • Build capability primitives such as pre‑built models, feature stores, AI‑assisted annotations, proactive insights
  • Develop modular and reusable intelligence components
• Applied AI/ML Features & Model Strategy
  • Select appropriate model families based on capability and cost constraints
  • Lead the design and deployment of CNN‑based, transformer‑based and LLM/VLM‑based models
  • Apply prompting, constraints, and reasoning structures to reduce hallucination
• Model Integration & Inference Optimization
  • Integrate sophisticated models with product functionality and ensure output reliability, consistency, safety, and user‑centric alignment
  • Develop logic for hybrid model usage
  • Collaborate with engineering to optimize latency, cost, and scalability
• Product Acuity
  • Bring strong product judgement to the development of the MLOps platform, balancing researcher/data scientist workflows with engineering, security, and compliance constraints.
  • Partner with multi‑functional collaborators to define user needs, success metrics, and a prioritized roadmap for platform capabilities.
  • Make pragmatic tradeoffs across build vs. buy, usability vs. flexibility, and time‑to‑value vs. long‑term scalability for ML platform components.

• MS or PhD in Machine Learning, Artificial Intelligence, Computer Science, or a related field.
• 10+ years of experience in machine learning engineering or applied AI, with significant ownership of production systems (or equivalent experience).
• Deep expertise in computer vision‑based deep learning.
• Demonstrated experience developing and deploying production models on cloud and edge.
• Strong product competence for ML platforms/MLOps, with the ability to translate multi‑functional needs into a clear roadmap and excellent developer/researcher experiences.
• Advanced proficiency in PyTorch and the modern ML ecosystem.
• Strong software engineering skills in Python and experience designing scalable ML systems.
• Proven track record to lead sophisticated technical initiatives and influence technical direction across teams.
• Excellent communication and teamwork skills.

• Experience building or working on ML or developer platforms for data scientists and researchers, including driving adoption through documentation, templates, and self‑serve workflows.
• Familiarity with MLFlow, Kubeflow, or similar MLOps platforms.
• Experience developing AI systems in regulated environments (e.g., SaMD), such as surgical, medical imaging, or healthcare‑related…