Staff Sustaining Reliability Engineer
at Rivianvw Tech
Palo Alto, California, remote, staff, full time, 5–10 years
posted 18 May 2026
- inverse power law
- Power BI
- Weibull analysis
- fault tree analysis
- Tableau
- 8D
- LLM
- SQL
- Arrhenius
- HEX
- Coffin-Manson
- Databricks
- Spark
- Python
- Ishikawa
About the role
As a Sustaining Reliability Engineer focused on Automotive Electronics, you will own the reliability
of Electronic Control Units (ECUs) across mass-production to the hands of customers. You will combine deep electronics reliability expertise with modern big data tooling (Databricks, Python, SQL, HEX) to build the systems that detect, model, and predict failures before they reach the field. This is a high-impact, cross-functional role at the intersection of hardware reliability engineering and data engineering.
What you would do
- Research and strategically implement vehicle, component, module, and system reliability testing to provide early detection of reliability issues during mass production through ongoing reliability testing, manufacturing validation testing, HASS testing, and Burn In testing
- Collaborate with quality, manufacturing, design, and process engineering to improve reliability outcomes; cascade reliability risks from design DFMEA to process PFMEA and control plans.
- Assess manufacturing changes and risks; develop test plans to validate them and own escalation and root-cause analysis for identified failures.
- Build and maintain automated data pipelines in Databricks to extract, transform, and load ECU fault signals, environmental stress telemetry (temperature, memory usage) into analysis-ready datasets.
- Develop and deploy real-time reliability modeling to surface current fleet risk exposure, predict impending ECU failures, and track trends across comparable vehicle populations.
- Build and maintain stakeholder-facing dashboards that integrate reliability model outputs, Jira tickets, and ECU test results (Validation, ESS, ORT) for rapid insight and decision support.
- Partner with an AI integration platform (e.g., Google Agent, Cursor) to accelerate ECU fault diagnosis, root-cause analysis workflows, and empirical grounding of future reliability requirements.
- Maintain closed-loop integration between field reliability data and the New Product Introduction (NPI) reliability teams to ensure field issues are captured and actioned.
What they are looking for
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