Functional Safety AI Principal Engineer

In this position...

Connect AI functional safety to software and quality engineering

Implement guardrails for safety within existing quality requirements enforced through the same development pipeline, quality gates, and metrics.

Drive system safety architecture and assurance strategy for AI across Ford’s ADAS/AD, driver-monitoring, and connected/embodied-AI programs;

Lead adoption of emerging AI-safety standards and process frameworks and prepares Ford for AI-specific regulation;

Principal technical authority bridging architecture, safety, software quality, AI/ML engineering, and program execution.

Day-to-day technical leadership on active programs, forward-looking work that matures Ford’s AI-safety, security and quality methods, tooling, and standards posture.

Assess AI maturity within projects, process frameworks and Ford infrastructure to develop an AI in FuSa Health Dashboard.

Work with Ford Connected Vehicle Software and Product Data Integrity, and supplier engineering teams to identify safety related process and work product gaps and recommend best practice solutions.

Develop a capability maturity plan working with engineering stakeholders.

Report to the Director of the Office of Functional Safety Assurance (OFSA).

Responsibilities

What you’ll do

• FuSa/AI Technical Leadership: Apply, demonstrate and champion best in class methods, tools, practices to ensure embedded software achieves safety.
• AI in FuSa Software Health Dashboard Ownership: Lead the development of the Health Dashboard for the enterprise, including its metrics and criteria for maturity. Develop AI enhanced tracking and analytic tools to determine status and priority actions.
• Strategic leadership: Provide strategic direction and leadership to the safety community, fostering a culture of collaboration, innovation, and excellence. Design and drive safe AI enablement strategies into cross-organizational teams.
• Ownership of the AI in FuSa strategy: Responsible for the definition and deployment of maturity model, framework for safe AI development and AI enhanced confirmation.
• Cross-functional collaboration: Collaborate closely with cross-functional teams (including product/program management, validation and verification, software, hardware, design, integration, quality, and architecture) to drive execution to program timelines. Cross-functional collaboration: Collaborate closely with cross-functional teams including product engineering, program management, validation and verification, software, hardware, design, integration, quality, and architecture to drive maturity of enterprise AI/FuSa Health metrics.
• FuSa Software related escapes: Apply system engineering, risk analysis, trouble shooting and root cause analysis methods to find software related issues and close the loop to process, methods and tools gaps.
• Alignment of FuSa, Cyber and Quality Assurance: Align and exploit synergies with SQA where this helps drive FuSa maturity.
• Continuous improvement: Drive continuous improvement initiatives to optimize software development and deployment processes, tools, and methodologies, enhancing effectiveness.
• Communication: Provide consistent health status, insights, risks and opportunities to executive leadership and stakeholders.
• Safety Strategy, Roadmap & Governance . Translate Ford’s AI-safety vision into an executable multi-year roadmap across ADAS/AD, driver monitoring, and connected/embodied-AI features; align near-, mid-, and long-term horizons and ensure clean transitions between them. Establish the AI functional-safety governance model: decision gates, safety sign-off authority, escalation paths, and accountability across programs and functions. Define and steward organization-wide policies, guidelines, and reference workflows for safe AI/ML development. Provide structured feedback to program and platform leadership on safety posture, gaps, and where investment is needed. Serve as design authority / final technical safety sign-off for AI safety concepts on assigned programs.
• AI/ML System Safety Architecture & Concept. Own the system safety architecture for AI-driven features: define the safety concept, allocate safety requirements, and specify fail-safe / fail-operational behavior, degradation strategies, and minimal-risk-condition (MRC) attainment. Translate learned/AI behavior into verifiable safety requirements, constraints, and runtime safety mechanisms (safety envelopes, monitors, doer/guardian architectures). Define architectural patterns for redundancy, diversity, plausibility checking, and out-of-distribution detection at vehicle and subsystem level. Specify the interaction between AI components and classical safety mechanisms (safety monitors, fallback controllers, arbitration). Establish safety-driven requirements across the data, model, and toolchain lifecycle.
• Safety of the Intended Function (SOTIF) & Performance Limitations. Lead SOTIF (ISO 21448) activities for AI-enabled features: triggering-condition analysis, scenario coverage, and residual-risk evaluation. Define ODD-based safety requirements and the evidence needed to demonstrate acceptable performance within the operational design domain and safe behavior at its edges. Drive systematic identification and mitigation of AI-specific hazards: distributional shift, edge cases, specification insufficiency, and emergent behavior.
• AI Safety Analysis, Verification & Validation. Apply and mature model validation, robustness and adversarial testing, fault injection, and runtime monitoring; identify gaps and feed them back into design. Lead hazard and safety analyses tailored to AI (STPA, HARA, FMEA/FMEDA) and integrate them with system-level analyses. Define validation strategies and acceptance criteria: statistical, scenario-based, simulation, and sim-to-real evidence validity. Establish metrics and KPIs for AI safety performance and assurance completeness.
• Software & AI/ML Quality Engineering. Embed AI safety requirements and accep