Design Reliability Engineer

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Design for Reliability (DfR) Engineer – Robotics & Digital R&D Team, Santa Clara, CA. Join our team to embed reliability thinking into product design, develop specifications for surgical robotic systems, and drive reliability analysis, fatigue and durability assessment, and test specification development to ensure products meet lifetime requirements.

• Translate product requirements into reliability targets, risk assessments, and verification plans for robotic hardware subsystems including actuators, motors, encoders, sensors, and cabling/connectors.
• Lead DFMEA reviews and recommend failure detections and mitigations for electro‑mechanical assemblies.
• Drive reliability‑focused design reviews, component selection, materials, and architecture decisions.
• Participate in issue/change reviews to evaluate impacts on reliability and testing.
• Develop and maintain reliability‑related specifications and lifecycle requirements for robotic subsystems.
• Identify operational loads/stresses and convert them into test inputs and load spectra.
• Perform fatigue/durability analysis, build damage models, and correlate with test and field data.
• Partner with test engineering to define and validate ALT and custom fatigue/durability tests.
• Capture lessons learned; update FMEAs, design standards, and test methods.
• Collaborate with Field Reliability and Clinical Engineering to integrate field/clinical feedback.
• Define reliability metrics and dashboards (life curves, damage accumulation, margin).

• Bachelor’s degree in Mechanical, Electrical, Materials, Biomedical, or equivalent engineering field.
• 2+ years of hands‑on experience in reliability engineering, DfR, structural fatigue, durability, or a closely related role.
• Experience developing and reviewing DFMEAs and translating failure modes into design/test requirements.
• Strong mechanical fatigue/durability fundamentals.
• Familiarity with engineering analysis tools (FEA, equivalent) and data‑analysis tools (Excel, MATLAB, Python, or similar).
• Familiarity with reliability/statistical methods (basic Weibull, life data analysis, confidence intervals, regression).
• Good understanding of materials behaviour, component selection trade‑offs, and environmental stressors.
• Excellent written and verbal communication skills.
• Experience working in multi‑functional teams.

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

Base pay range: $106,000 to $170,200. Eligible for annual performance bonus in accordance with company plan. Benefits include medical, dental, vision, life insurance, short‑ and long‑term disability, business accident, group legal insurance, and a consolidated retirement plan (401(k)). Additional benefits: vacation (up to 120 hours per calendar year), sick time (up to 40 hours), holiday pay (up to 13 days), and work‑personal‑family time (up to 40 hours).

Eligible for long‑term incentive program.

Johnson & Johnson is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, protected veteran status or other characteristics protected by federal, state or local law. We actively seek qualified candidates who are protected veterans and individuals with disabilities under VEVRAA and Section 503 of the Rehabilitation Act.

Johnson & Johnson is committed to providing an interview process that is inclusive of applicants’ needs.