Interested in this AI/ML Engineer role at Ford Motor Company?
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Overview
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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
We are the movers of the world and makers of the future. We get up every day, roll up our sleeves, and build a better world – together. At Ford, we’re all a part of something bigger than ourselves. Are you ready to change the way the world moves?
Integrated Services, including the Digital Product team at Ford, is undergoing an exciting evolution as we accelerate how we create, launch and go\-to\-market with leading digital experiences for customers inside and outside of the vehicle. Central to this acceleration is our ability to strategically and responsibly use AI, automation and intelligent workflows to fundamentally transform how we operate across Integrated Services organization and the Ford enterprise, including digital product management, vehicle development and planning, Project Management Office (PMO) and go\-to\-market.
In this position...
Reporting to the Global Head of Digital Product, Integrated Services, the AI Transformation Architect is a hands\-on builder and transformation leader responsible for identifying, redesigning and scaling how Ford’s Integrated Services organization ideates, assess, plans and executes our work by embedding AI into the highest\-value workflows to improve speed, quality, decision\-making, and customer impact across the team. This role sits at the intersection of product management, engineering, operations/PMO, vehicle development and cycle planning, and business teams, translating high\-friction workflows into practical, AI\-enabled systems that help us deliver better experiences faster and create leverage for the organization and our internal partners.
The ideal candidate is deeply technical, highly pragmatic – they must understand how AI agents work, how to configure and extend them, and how to apply them in real\-world enterprise contexts. This role partners closely with Integrated Services leadership, as well as functional leaders across the enterprise (Security, Office of General Counsel, Privacy, Enterprise Technology, etc.) to ensure AI\-enabled workflows are built responsibly, governed appropriately, and scaled with confidence. This person should be comfortable working with tools and patterns such as APIs, retrieval, workflow orchestration, evaluations, agent instructions, skill creation and human\-in\-the\-loop flows. Familiarity with MCPs, CLIs, and AGENT.md\-style configurations patterns is a plus, but the primary requirement is fluency in how modern AI\-enablement workflows are built, deployed, governed and scaled. The ideal candidate will have a deep understanding of how teams work effectively in large, matrixed organizations across the hardware \+ software \+ customer experience loop, how to influence change in these organizations, and how to turn new capabilities into measurable business and customer impact.
This is a hands\-on transformation role: the person will build and prototype directly, but success will be measured by scaled adoption, durable workflow change, and measurable improvement in how the Integrated Services organization operates.
What you'll do...
- Transform the Integrated Services Product Team Operating Model: Identify, redesign, and scale AI\-enabled workflows across discovery, customer research, Product Requirement Document (PRD) roadmaps, prioritization, launch readiness, executive reviews, analytics, PMO, go\-to\-market, and post\-launch learning.
- Build AI Agents, Automation and Infrastructure: Create, configure, test, and maintain AI agents, tools, workflows, and integrations using APIs, retrieval, orchestration, agent instructions, approved AI platforms, enterprise systems, and related technical patterns.
- Prototype, Pilot and Scale: Rapidly prototype with real users, validate workflow fit, identify failure modes, and move successful solutions from idea to prototype to pilot to scaled adoption, working through technical, operational, security, privacy, and change\-management barriers.
- Create Reusable Capabilities and Playbooks: Build repeatable agents, templates, standards, workflows, and best practices for common product, PMO and GTM work, including customer insight synthesis and journey mapping, requirement generation, PRD review, competitive analysis, roadmap tradeoffs, launch planning and execution, risk reviews, business case development, and executive communication.
- Measure Impact: Define and track outcomes tied directly to business and customer value creation, including adoption, time saved, cycle\-time reduction, decision speed, employee experience, quality improvements, rework reduction, revenue generation and business or customer outcomes from AI enabled workflows.
- Drive Adoption and Behavior Change: Train teams, coach leaders, create champions, document best practices, and make AI\-enabled workflows easy, safe and useful enough to become the default way of working.
- Establish Quality, Governance and Trust: Partner with security, legal, privacy, IT, PMO, go\-to\-market and engineering to ensure workflows are secure, compliant, auditable, explainable, and appropriate for enterprise use.
- Design Human\-In\-The\-Loop Systems: Define where AI should act independently, where humans must review or approve, and how teams should manage risk, quality and accountability in AI\-enabled workflows.
- Increase Leverage: Identify and automate repetitive, manual, duplicative, or low\-value work so teams can spend more time on customer insight, product judgment, and execution.
- Evaluate Emerging AI Patterns: Stay current on AI agent frameworks, tooling, security models, governance practices, workflow automation approaches, and enterprise patterns, then translate the most practical opportunities into Ford use cases.
You'll have...
- Strong technical fluency and a hand\-on builder mindset, with familiarity across modern AI workflow patterns including APIs, retrieval, agent instructions, orchestration, evaluations, and human\-in\-the\-loop flows.
- Experience building AI\-enabled workflows, automations, internal tools, or agent\-based systems that were adopted by real users, not just prototypes.
- Strong product management fluency including discovery, customer research, PRDs, roadmaps, prioritization, launch readiness, stakeholder alignment and product analytics
- Experience redesigning knowledge\-work workflows or operating models at team or enterprise scale.
- Ability to rapidly prototype, test with users, measure outcomes, and iterate.
- Comfort working within enterprise constraints, including security, privacy, legal, IT, data access, and governance.
- Ability to influence cross\-functional teams and drive adoption without relying solely on formal authority.
- Strong communication, documentation, training, storytelling, and collaboration skills, with the ability to bring skeptical teams along.
Even better, you may have...
- Experience in automotive, digital product, software, platform, or large\-scale enterprise organizations.
- Enterprise implementation experience, especially in environments with security, privacy, identify, legal, compliance, procurement, IT and data\-access constraints.
- Experience creating training, enablement, champion networks and reusable playbooks for scaled adoption.
- Background in workflow automation, developer tools, internal tooling, product operations, design operations, or AI enablement.
- Experience building tools for teams rather than only consumer\-facing applications.
- Experience evaluating AI platforms, vendors, agent frameworks, and emerging enterprise AI tooling.
- Understanding of AI safety, data governance, access control, auditability, and responsible AI practices.
- Experience working with product, engineering, design, analytics, business operations, legal, security, and IT teams in a complex matrixed environment.
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 childcare 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 a leadership level 4\.
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/LL4Benefits
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\.
This position is hybrid (onsite four days per week) for candidates who are in commuting distance to a Ford hub location or remote for non\-local candidates.
\#LI\-Hybrid \#LI\-WC2
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 in Demand for This Role
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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Ford Motor Company AI Hiring
Ford Motor Company has 7 open AI roles right now. They're hiring across Data Scientist, AI Product Manager, AI Software Engineer, AI/ML Engineer. Positions span Dearborn, MI, US, Palo Alto, CA, US. Compensation range: $192K - $250K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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