Senior Sensor Hardware Safety Engineer
This role is currently open. See the posting.
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Role summary
Our Hardware Safety Engineering team is seeking a highly motivated Sr Sensor Hardware Safety Engineer to define and drive hardware safety for sensor systems. You will own the end-to-end hardware safety strategy for cameras, LiDAR, radar, inertial measurement units (IMUs), and other sensors across the Company's physical AI platforms. You will collaborate across sensor architecture, board hardware, software, systems, validation, and supplier teams. Together, you will evaluate sensor safety concepts and ensure robust integration into safety-critical systems. This is an outstanding opportunity to work where advanced sensing, high-performance computing, and safety engineering converge.
Responsibilities
- Own the end-to-end sensor hardware safety strategy for cameras, LiDAR, radar, IMUs and other sensors used in the Company automotive systems.
- Support sensor supplier selection through technical and safety evaluations of sensor architectures, safety concepts, safety analysis, and integration assumptions.
- Define and manage sensor hardware safety requirements, including requirements for sensor power, interfaces, timing, synchronization, data integrity, fault detection, and fault response.
- Collaborate with sensor architects, board and system designers, software teams, and validation engineers to develop effective implementations of sensor safety requirements.
- Perform and review safety cases & analyses, including FMEAFailure Modes and Effects Analysis: a bottom-up method cataloging how components fail and what effects those failures produce. Read more, FMEDAFailure Modes, Effects and Diagnostic Analysis: quantitative failure analysis producing safe/dangerous failure rates and diagnostic coverage. Read more, DFA, and FTA, and use the results to guide architecture decisions and design improvements.
- Work with third-party suppliers to evaluate safety manuals, analysis reports, assumptions of use, and supporting evidence for sensor integration.
- Develop clear safety documentation, integration guidance, analysis reports, and user-facing safety information for internal teams and customers.
Requirements
- Bachelor's degree in electrical engineering, computer engineering, computer science, or a related engineering field, or equivalent experience.
- 8+ years of relevant experience in automotive sensors, hardware systems, functional safety, or a related field.
- Understanding of automotive sensor technologies such as cameras, radar, LiDAR, or IMUs, including their hardware architectures and system interfaces.
- Knowledge of safety analysis methodologies, including FMEA, FMEDA, DFA, and FTA.
- Ability to work effectively with sensor suppliers and multidisciplinary engineering teams from concept through integration and verification.
- Proven ownership and proactivity, with a strong bias for action and results.
- Excellent analytical, written, and verbal communication skills.
Nice to have
- Experience developing automotive sensor systems or evaluating sensor safety concepts and architectures.
- Understanding of functional-safety development processes required for ISO 26262Road Vehicles - Functional Safety. Automotive functional safety standard defining ASILAutomotive Safety Integrity Level (A to D) under ISO 26262, derived from severity, exposure, and controllability. Read more levels and the automotive safety lifecycle. Read more or other relevant industry standards such as IEC 61508Functional Safety of E/E/PE Safety-Related Systems. The umbrella functional safety standard defining the safety lifecycle, SILA discrete level (SIL 1 to SIL 4) specifying the required risk reduction and dependability of a safety function under IEC 61508 and its sector standards. Read more levels, and requirements for electrical, electronic, and programmable systems. Read more and ISO 13849.
- Experience defining or verifying safety mechanisms, and writing requirements, for safety-critical sensor hardware and systems.
- Experience in PCB or ECU design and embedded systems based on the Company SoCs.
- Background in system modeling using SysML and developing structured user documentation such as DITA.
The Company empowers automakers, developers, manufacturers, researchers, and startups to build and deploy physical AI systems. Its unified AI computing architecture enables training advanced AI models on the Company accelerated computing platforms and deploying them into safety-critical systems, with software updates that add and improve capabilities throughout the product lifecycle.