Functional Safety AI Principal Engineer

Ford Motor Company • Dearborn, MI

12h • $141,700 - $268,300 • Hybrid

About The Position

In this position, you will connect AI functional safety to software and quality engineering by implementing guardrails for safety within existing quality requirements enforced through the same development pipeline, quality gates, and metrics. You will drive system safety architecture and assurance strategy for AI across Ford’s ADAS/AD, driver-monitoring, and connected/embodied-AI programs. You will lead the adoption of emerging AI-safety standards and process frameworks and prepare Ford for AI-specific regulation. This role serves as a principal technical authority bridging architecture, safety, software quality, AI/ML engineering, and program execution. Your day-to-day will involve technical leadership on active programs and forward-looking work that matures Ford’s AI-safety, security, and standards posture. You will assess AI maturity within projects, process frameworks, and Ford infrastructure to develop an AI in FuSa Health Dashboard. You will 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. You will develop a capability maturity plan working with engineering stakeholders. This position reports to the Director of the Office of Functional Safety Assurance (OFSA).

Requirements

• M.S. in Electrical Engineering, Computer Engineering, or Embedded Systems, with substantial industry experience.
• Deep, hands-on command of the automotive AI-safety core, all mandatory: ISO 26262 (functional safety), ISO 21448 (SOTIF), and ISO/PAS 8800 (safety and AI).
• Working knowledge of the broader assurance and governance landscape (UL 4600, ISO/IEC TR 5469 and ISO/IEC 23894), with the judgment to apply each where it fits.
• Familiarity with the AI and vehicle-safety regulatory and governance landscape (EU AI Act, the EU General Safety Regulation and type-approval framework, UNECE regulations, US NHTSA/FMVSS frameworks, and the NIST AI Risk Management Framework), and the ability to turn regulatory obligations into engineering and evidence requirements.
• Experience with Automotive SPICE and software/ML quality engineering: quality gates, metrics, test strategy, and MLOps practice; experience supporting process, quality, or safety assessments and audits a strong plus.
• 10+ years architecting, designing, and validating safety-critical embedded systems in automotive environments.
• Solid understanding of AI/ML in embedded and real-time systems, and of AI-specific failure modes.
• Proven delivery across complex, cross-functional programs; able to bridge architecture, safety, and execution teams.
• Standards-body and/or regulatory engagement experience (ISO, SAE, UNECE) desirable.
• Technical-leadership track record; prior people-management experience a plus where the role carries team scope.
• Communication & collaboration: Excellent communication and collaboration skills, with the ability to effectively engage with cross-functional teams, external partners, and stakeholders to drive successful feature system development projects.
• Leadership: Proven leadership abilities, with a track record of mentoring and guiding technical teams, fostering innovation, and delivering high-quality software that meets safety, security, and performance objectives.
• Strategic & systemic thinking: Demonstrated ability to comprehend end-to-end systems of systems, apply systemic thinking, critical thinking, and strategic reasoning to identify opportunities, rapidly understand complex concerns, provide alternative solutions, and drive strategy from concept through to production.
• Adaptability: Comfort with ambiguity, with a demonstrated ability to structure an approach to complex problems and drive clarity on solutions.
• Innovation: Understand and drive innovation and change in processes, engineering excellence, quality, and efficiency.
• Continuous improvement mindset: A drive for a system approach to design and development, coupled with a desire and curiosity to strive for exceptional delivery execution and continuous improvement.

Nice To Haves

• Ph.D in Electrical Engineering, Computer Engineering, or Embedded Systems, with substantial industry experience.

Responsibilities

• 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. 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 acceptance criteria into the software quality management system and stage-gate quality reviews, so safety is enforced through the quality pipeline rather than maintained alongside it. Extend software quality practice to ML artifacts: treat data, models, and pipelines as controlled quality items with versioning, reproducibility, traceability, and defined quality gates (MLOps quality). Define and track quality metrics for AI-enabled software: code quality and coverage, model performance and robustness scores, data-quality indicators, and defect/escape rates; drive them into release criteria and quality dashboards. Align AI/ML development with Automotive SPICE (ASPICE) and its emerging ML/AI extensions; help mature process capability for data management, model training, and model validation. Support and interface with quality and process assessments and audits (ASPICE, functional-safety audits, tool confidence / tool qualification), and drive findings to closure with corrective actions. Establish field-quality feedback loops (production monitoring, drift detection, and incident analysis) that feed back into both quality and safety improvement.
• Assurance Cases & Safety Argumentation. Develop and maintain structured assurance/safety cases (e.g., GSN) for AI-enabled systems, demonstrating absence of unreasonable risk. Integrate ISO 26262, ISO 21448, ISO/PAS 8800, and UL 4600 evidence into a coherent, auditable argument. Prepare safety documentation and readiness for internal reviews, independent assessors, certification bodies, and regulators.
• Standards, Process & Software-Quality Integration. Lead adoption of the emerging AI-safety standards portfolio into Ford’s product-development and safety lifecycle: ISO/PAS 8800 (safety and AI for road vehicles), ISO/IEC TR 5469 (functional safety and AI systems), ISO/IEC 23894 (AI risk management), and UL 4600 (autonomous-product safety cases). Anchor process and product quality to the software-quality standards base: Automotive SPICE (and its ML/AI extensions), ISO/IEC/IEEE 12207 (Software life cycle processes), ISO/IEC 25059 (AI-system quality model), and ISO/IEC 5338 (AI-system lifecycle), together with ISO 26262 Parts 6 and 8 for software development and supporting processes. Integrate these with the established automotive safety and cybersecurity baseline: ISO 26262, ISO 21448 (SOTIF), ISO/SAE 21434, and the ISO/TS 5083 (AV Safety) framework for automated driving. Draw on adjacent AI-governance and risk frameworks where useful, such as the NIST AI Risk Management Framework (its Govern / Map / Measure / Manage functions), ISO/IEC 42001 (AI management systems), and ISO/IEC 5259 (data quality for ML). Define which standard governs which claim in the safety and quality argument, and update development processes, work products, and tailoring guidance to incorporate AI-specific requirements. Represent Ford in external standards development (ISO, SAE, UNECE) and translate emerging requirements into internal readiness.
• Regulatory Readiness & Compliance Strategy. Prepare Ford to meet AI-specific regulation, with the EU AI Act (Regulation (EU) 2024/1689) as the anchor: interpret how its high-risk obligations (risk management, data governance, technical documentation, human oversight, robustness, logging, and transparency) apply to Ford’s AI-enabled systems. Track how those obligations reach automotive AI through the vehicle type-approval route (Annex I / Regulation (EU) 2018/858 and the General Safety Regulation (EU) 2019/2144). Maintain a forward view of UNECE activity (UN Regs No. 157 and 152, the DCAS work, and the automated-driving framework) and of US frameworks (NHTSA guidance, FMVSS), and advise programs on divergence across markets. Structure Ford’s AI risk-governance program around a recognized backbone such as the NIST AI Risk Management Framework, using its crosswalks to the EU AI Act and ISO/IEC 23894 (guidance on risk management) so governance activities (govern, map, measure, manage) stay consistent across markets and the resulting records are reusable as regulatory evidence. Establish the compliance evidence and documentation trail (technical files, risk-management records, data-governance artifacts, and post-market monitoring) that type-approval authorities and market surveillance will expect for AI features, aligned to the assurance case so evidence is produced once and reused. Advise leadership on regulatory strategy, timelines, and readiness gaps; serve as internal point of contact for AI-regulatory questions and, where appropriate, as external liaison.
• Cross-Functional & Supplier / Tier-1 Collaboration. Bridge architecture, safety, software quality and SQA, test engineering, AI/ML engineering, systems, and program-execution teams so safety and quality are designed in rather than bolted on. Define and flow down AI safety requirements to suppliers and Tier-1 partners; review supplier safety cases and evidence, and own the relevant interface/DIA agreements, including the provider/deployer responsibility split that EU AI-Act conformity turns on. Coordinate AI safety across product-development milestones, applications, and platform programs. Support partner-, customer-, and assessor-facing safety discussions.
• Technical Leadership, Mentorship & Capability Building. Serve as principal technical authority and mentor for AI functional safety; grow internal competency across the organization. Build and curate the methods, tools, templates, and training that scale AI-safety practice. Lead and grow a dedicated AI-safety team: set direction, ensure delivery to full potential, and create clear career paths. Represent Ford’s AI-safety thought leadership internally and externally through publications, conferences, and working groups.
• Innovation & Applied Research. Drive innovation projects for safe AI aligned to the roadmap; shepherd concepts from research through productization. Evaluate emerging AI-safety methods, tools, and academic advances for applicability to Ford programs. Pilot novel assurance techniques (runtime assurance, formal methods,

Benefits

• Immediate medical, dental, vision and prescription drug coverage
• Flexible family care days, paid parental leave, new parent ramp-up programs, subsidized back-up child care and more
• Family building benefits including adoption and surrogacy expense reimbursement, fertility treatments, and more
• Vehicle discount program for employees and family members and management leases
• Tuition assistance
• Established and active employee resource groups
• Paid time off for individual and team community service
• A generous schedule of paid holidays, including the week between Christmas and New Year’s Day
• Paid time off and the option to purchase additional vacation time.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal