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About This Role
Overview
At Ford, you’ll work on ideas that matter, alongside passionate people who want to make a global impact. Together, we’re shaping the next era of transportation—grounded in purpose, driven by progress. Make your move.
- Job Type: Full time
- Work Type: Hybrid
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).
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 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. (optional; include only if the role carries people-management scope). Represent Ford’s AI-safety thought leadership internally and externally through publications, conferences, and working groups. (optional).
- 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,
You’ll have…
- 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.
Even better, you may have...
- Ph.D in Electrical Engineering, Computer Engineering, or Embedded Systems, with substantial industry experience.
You may not check every box, or your experience may look a little different from what we've outlined, but if you think you can bring value to Ford Motor Company, we encourage you to apply!
As an established global company, we offer the benefit of choice. You can choose what your Ford future will look like: will your story span the globe, or keep you close to home? Will your career be a deep dive into what you love, or a series of new teams and new skills? Will you be a leader, a changemaker, a technical expert, a culture builder…or all of the above? No matter what you choose, we offer a work life that works for you, including:
- 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.
This position is leadership level 5 and ranges from $141,700-$268,300.
Final determination of salary grade will be based on candidate's skills and experience, and base salary will be set within the applicable range according to job scope, responsibility and competitive market value.
For more information on salary and benefits, click here: https://fordcareers.co/LL5
Visa sponsorship is not available for this position.
Candidates for positions with Ford Motor Company must be legally authorized to work in the United States. Verification of employment eligibility will be required at the time of hire.
We are an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, religion, color, age, sex, national origin, sexual orientation, gender identity, disability status or protected veteran status. In the United States, if you need a reasonable accommodation for the online application process due to a disability, please call 1-888-336-0660.
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Salary Context
This $141K-$268K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Ford Motor Company, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. Disclosed range: $141K to $268K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Ford Motor Company AI Hiring
Ford Motor Company has 6 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect, Data Scientist. Positions span Dearborn, MI, US, Remote, US, Dearborn, MI. Compensation range: $192K - $268K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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