Staff Engineer, Ai/ Ml/surgical Robotics - Ottava

Johnson & Johnson Johnson & Johnson · Pharma · Santa Clara, CA +1

Staff Engineer AI/ML for Digital Manufacturing at Johnson & Johnson, focusing on developing and deploying analytics and ML solutions for supply chain operations. The role involves leading end-to-end delivery of ML solutions, including data ingestion, feature engineering, model development, validation, deployment, and lifecycle management, with a strong emphasis on MLOps and adherence to regulated manufacturing standards. Key responsibilities include designing production-grade pipelines, real-time inference, and translating manufacturing challenges into measurable use cases.

What you'd actually do

  1. Define technical requirements and architecture for analytics and AI/ML solutions across manufacturing environments (edge, OT, and cloud).
  2. Lead end-to-end delivery of analytics and ML solutions, including data ingestion, feature engineering, model development, validation, deployment, and lifecycle management.
  3. Design, implement, and operate production-grade pipelines and inference (batch and real time), with monitoring and SLAs for latency, availability, and throughput.
  4. Translate manufacturing challenges (yield, downtime, quality, throughput) into measurable use cases with clear KPIs and expected ROI.
  5. Establish MLOps and governance practices (model versioning, experiment tracking, reproducibility, access control, audit trails) aligned to regulated manufacturing expectations (CSV, GxP where applicable).

Skills

Required

  • Python
  • ML libraries (scikit-learn, TensorFlow, PyTorch)
  • data processing frameworks (Spark/PySpark)
  • MLOps experience (orchestration, CI/CD, model serving, monitoring/observability, automated retraining, experiment tracking)
  • SQL
  • data modeling

Nice to have

  • manufacturing and industrial data sources (MES, OPC UA, PLC logs, telemetry, sensors)
  • lakehouse

What the JD emphasized

  • delivering ML/AI solutions into production at scale
  • manufacturing or industrial/OT environments
  • regulated industries preferred
  • regulated manufacturing expectations (CSV, GxP where applicable)

Other signals

  • production-grade pipelines
  • MLOps
  • regulated manufacturing expectations
  • predictive quality
  • anomaly detection
  • predictive maintenance
  • process optimization