Staff Ai/ml Software Engr, Navigation

Johnson & Johnson Johnson & Johnson · Pharma · Boston, MA +2

Staff AI/ML Software Engineer at Johnson & Johnson's Orthopaedics division, focusing on developing and integrating AI/ML for surgical navigation and planning systems. The role involves building production-ready AI/ML systems, mentoring junior engineers, and contributing to regulatory documentation within a regulated healthcare environment.

What you'd actually do

  1. Be a key contributor in the evolution of DePuy Synthes’ AI/ML platform for flexible machine learning model training, fine-tuning, transformation and evaluation use cases across various surgical navigation initiatives.
  2. Build production ready, on‑device and cloud-based, AI/ML systems with real clinical impacts to our customers.
  3. Drive model improvements using the latest techniques and approaches for evaluation and tuning.
  4. Contribute to regulatory documentation, traceability, and V&V aligned with IEC 62304, ISO 14971, and related guidance; support submissions (e.g., FDA 510(k)) as needed.
  5. Collaborate in Agile/Scrum workflows, mentor junior engineers, and lead design/code reviews to sustain long‑term maintainability.

Skills

Required

  • Python
  • Machine Learning fundamentals
  • supervised and unsupervised learning
  • model selection
  • cross-validation
  • evaluation metrics
  • ML frameworks (Tensorflow, Keras, Pytorch, scikit-learn)
  • ML model training/optimization
  • local/cloud (AWS Sagemaker, Azure Studio)
  • AI systems in regulated environments (SaMD)
  • technical documentation for AI/ML
  • full SDLC in a regulated environment
  • communication
  • collaboration
  • influence
  • architecture and design reviews

Nice to have

  • Agentic AI
  • Generative AI
  • LLMs
  • on-device and cloud-based AI/ML implementations
  • AI-driven development and testing
  • object-oriented programming languages
  • requirements, design documentation in tools like Polarion, JAMA

What the JD emphasized

  • AI/ML platform
  • machine learning model training
  • fine-tuning
  • evaluation
  • production ready
  • on-device and cloud-based
  • regulated environments
  • traceability
  • validation
  • auditability
  • IEC 62304
  • ISO 14971
  • FDA 510(k)

Other signals

  • AI/ML platform development
  • production-ready AI/ML systems
  • regulated environments