Staff Sustaining Reliability Engineer

Rivian and Volkswagen Group Technologies•Palo Alto, CA

8d•$186,000 - $232,500

About The Position

Rivian and Volkswagen Group Technologies is a joint venture between two industry leaders with a clear vision for automotive’s next chapter. From operating systems to zonal controllers to cloud and connectivity solutions, we’re addressing the challenges of electric vehicles through technology that will set the standards for software-defined vehicles around the world. The road to the future is uncharted. By combining our expertise across connectivity, AI, security and more, we’ll map a new way forward. Working together, we’ll create a future that’s more connected, more intelligent, more sustainable for everyone. 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.

Requirements

5–10 years of industry experience in a reliability engineering role, preferably in automotive electronics or embedded systems.
Bachelor of Science in Electrical Engineering, Mechanical Engineering, Systems Engineering, or a related discipline; advanced degree a plus.
Hands-on experience with electronics reliability disciplines: physics of failure, failure mode analysis, fatigue/degradation mechanisms in PCBAs and ECUs.
Knowledge of reliability statistics including Weibull analysis, accelerated life testing models (Arrhenius, Coffin-Manson, inverse power law), and reliability growth modeling.
Expert understanding and demonstrated application of root-cause analysis techniques (8D, Ishikawa, fault tree analysis).
Experience establishing reliability requirements, test programs, and deploying reliability testing guidelines for Ongoing Reliability Testing (ORT) and Highly Accelerated Stress Screening (HASS).
Proficiency in SQL and Python for data manipulation, statistical analysis, and pipeline development; experience with big-data platforms such as Databricks, Spark, or equivalent.
Experience with dashboard development tools (HEX, Tableau, Power BI, or similar) for visualization and stakeholder reporting.
Experience integrating or prompting AI/LLM tools for engineering workflows (fault diagnosis, requirement generation).

Nice To Haves

advanced degree a plus

Responsibilities

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.