Principal AI Lead - Surgical AI

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and Med Tech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn

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As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world.  We provide an inclusive work environment where each person is considered as an individual.  At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.

** Job Function:*
* R&D Product Development

** Job Sub*
* ** Function:*
* R&D Machine Learning

** Job Category:*
* Scientific/Technology

** All Job Posting Locations:*
* Santa Clara, California, United States of America

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* Job Description:

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* ** Overview*
* 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 by synchronizing devices, data and AI driven insights in one place to simplify access, build confidence and unify a non-linear care experience.

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.

** What You Will Do*
* ** 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.

**…