Design for Reliability Engineer

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

Johnson & Johnson MedTech is seeking a Design for Reliability (DfR) Engineer to embed reliability thinking into product design teams for robotic surgical systems. The role involves developing reliability targets, conducting risk assessments, leading DFMEA reviews, driving reliability-focused design reviews, and developing specifications for robotic hardware subsystems. Responsibilities include performing fatigue/durability analysis, correlating with test and field data, and defining reliability metrics. Requires a Bachelor's degree and 2+ years of experience in reliability engineering, DfR, or structural fatigue/durability.

What you'd actually do

  1. Translate product requirements into reliability targets, risk assessments, and verification plans for robotic hardware subsystems including actuators, motors, encoders, sensors and cabling/connectors.
  2. Lead DFMEA reviews and recommend failure detections and mitigations for electro-mechanical assemblies
  3. Drive reliability-focused design reviews (component selection, materials, architectures).
  4. Participate in issue/change reviews to evaluate impacts on reliability and testing.
  5. Develop and maintain reliability related specifications and lifecycle requirements for robotic subsystems.

Skills

Required

  • reliability engineering
  • DfR
  • structural fatigue
  • durability
  • developing and reviewing dFMEAs
  • mechanical fatigue/durability fundamentals
  • engineering analysis tools (e.g., FEA tools for stress analysis, or equivalent)
  • data-analysis tools (Excel, MATLAB, Python, or similar)
  • reliability/statistical methods (basic Weibull, life data analysis, confidence intervals, regression)
  • materials behavior
  • component selection trade-offs
  • environmental stressors
  • multi-functional teams

Nice to have

  • Advanced Degree in Mechanical Engineering, Reliability Engineering, Materials Science, or similar
  • developing and validating damage accumulation models and advanced reliability models
  • test-spec development and correlating test results with analytical models and field data
  • accelerated life-test program design and interpretation
  • correlating telemetry/field-measured loads to lab tests and integrating field-derived load spectra into test programs
  • test instrumentation and data acquisition systems for durability tests
  • advanced data analysis using Databricks, SQL, etc.