Staff Hardware Systems Applications Engineer
- Pittsburgh, PA
- Permanent
- Full-time
- Lead Root Cause Investigations: Own and drive the end-to-end Root Cause and Corrective Action (RCCA) process for critical hardware (HW) and software (SW) issues reported from the field.
- Develop Scalable Processes: Work cross-functionally between engineering, service, and operational teams to improve, establish, and harden scalable processes for field problem management, ensuring learnings are incorporated into future product generations and service/diagnostic procedures.
- Cross-Functional Collaboration: Serve as a primary technical point of contact, orchestrating investigations across engineering teams including Systems, Software, Hardware, Test, and Manufacturing to diagnose and resolve systemic problems.
- Drive Product Improvement: Translate investigation findings into actionable design, manufacturing, or software changes. Champion corrective and preventative actions to enhance product robustness and prevent issue recurrence.
- Communicate and Report: Clearly and concisely communicate investigation status, findings, and recommendations to technical peers, program managers, and executive leadership.
- Bachelor's degree in Mechanical Engineering, Electrical Engineering, Computer Science, or a related technical field.
- 8+ years of relevant experience in systems engineering, test engineering, or product support for complex hardware/software products.
- Demonstrated mastery of formal RCCA methodologies (e.g., 8D, Fishbone/Ishikawa diagrams, 5 Whys, Fault Tree Analysis).
- Proficiency in scripting languages (e.g., Python, SQL) for data manipulation and analysis.
- Deep well of experience troubleshooting integrated HW/SW systems.
- Strong communication skills, both verbal and written, with the ability to convey complex technical concepts to non-technical stakeholders.
- Willingness to travel on a semi-frequent basis to investigate field issues and share knowledge at other AUR locations
- Master's in an engineering discipline.
- 10+ years of experience in the robotics, autonomous vehicle, or aerospace industry.
- Expert-level experience using Vector CANalyzer, CANoe, and other similar vehicle network analysis tools.
- Experience with C++ for log parsing and analysis.
- A proven track record of successfully leading multiple, complex, cross-functional investigations from start to finish.
- Strong understanding of statistical analysis methods for reliability and failure analysis.