Diagnostics Engineer -

Vehicle System Integration Location:

Foster City, CA Key Responsibilities

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Diagnostic Algorithm Design & Implementation:

Architect and implement real-time diagnostic monitors in modern C++, including signal-level plausibility checks, degradation detection, and fault isolation logic. Perform software unit testing, HIL testing, and automated regression tests to validate diagnostic algorithms prior to final implementation

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Diagnostic Requirement Engineering:

Author formal diagnostic monitor requirements (detection thresholds, debounce strategies, fault response actions) traceable to system-level or component level failure modes; verify requirements against fleet and bench data.

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Data-Driven Analysis & Validation:

Leverage large-scale fleet data in Databricks to characterize faults, tune detection thresholds, validate algorithm performance (detection rate, false-positive rate), and drive continuous improvement.

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Fault Troubleshooting & Root Cause Analysis:

Lead investigation of field-reported faults using telemetry, logged data, and replay tools; identify root causes spanning hardware, firmware, calibration, and environmental factors.

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Documentation & Knowledge Transfer:

Produce clear technical specifications, design documents, and service procedures that enable Fleet Operations technicians to diagnose and resolve faults efficiently.

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Continuous Improvement:

Monitor fleet-wide diagnostic KPIs; propose and implement algorithm refinements, new monitors, and process improvements. Qualifications

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Education:

Bachelor's degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, Robotics, Aerospace Engineering, or a related technical field. Master's degree preferred

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Experience:

6-8 years of professional experience in diagnostics, embedded controls, or vehicle systems engineering within the automotive, autonomous vehicle, aerospace, or robotics industry. Required Skills

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Vehicle Diagnostics Expertise:

Working knowledge of diagnostic standards and protocols (UDS / ISO 14229, DTC management, OBD-II / SAE J1979) and communication buses (CAN, LIN, Automotive Ethernet).

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Data Analysis:

Proficiency with Python for data analysis and visualization; experience working with large-scale telemetry datasets (Databricks, or equivalent).

• C++

Proficiency:

Strong, demonstrable C++ development skills in production or safety-critical environments;

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Control & Estimation Theory:

Hands-on experience designing and implementing control algorithms, state estimators, or model-based diagnostic observers.

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Problem Solving:

Proven ability to systematically decompose complex, multi-domain problems and drive them to root cause under ambiguity.

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Communication:

Excellent written and verbal communication skills; able to present technical analyses and recommendations to both engineering peers and non-technical stakeholders. Preferred Skills

• Experience with implementing prognostics or vehicle health monitoring

• Experience with HIL/SIL testing frameworks for diagnostic validation.