Interested in this AI/ML Engineer role at Ford Motor Company?
Apply Now →Skills & Technologies
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: Remote
In this position...
This role requires a leader who brings deep technical knowledge of enterprise AI platforms and the strategic judgment to look across every platform EPEO offers — spanning leading AI and cloud platform providers — and define where AI belongs, what it should change, and how the organization gets there. This is not an operational role. It is the most forward\-looking mandate in the platform organization: a cross\-cutting AI strategy and enablement function with authority across all platform organizations.
The Senior Director, AI Platform Engineering \& Enablement brings two equally weighted mandates. On the engineering side, this leader applies deep technical judgment to evaluate, architect, and govern how AI capabilities are integrated across EPEO’s platform portfolio — ensuring what is deployed is the right capability, properly engineered, and fit for the business functions it serves. On the enablement side, this leader ensures AI capabilities from our major providers are not just procured but actively activated, governed, and embedded into how our platforms operate. This person does not wait for the organization to ask what AI can do. They already know, and they have a plan.
What you'll do...
AI Portfolio Strategy \& Platform Assessment
Own the cross\-platform AI strategy across all of EPEO’s platform portfolio:
- Maintain a comprehensive, enterprise\-wide view of how AI is strategically positioned across every platform EPEO operates — what is deployed, what it is being used for across business functions, whether it is the right capability for the problem, and where gaps or redundancies exist
- Continuously evaluate AI capabilities across all major providers and platforms against EPEO’s current operating model:
+ Leading cloud platform AI services and infrastructure
+ Enterprise AI assistant and productivity platforms
+ Frontier AI model providers and API platforms
+ Emerging AI providers and infrastructure primitives
- Define and own the cross\-platform AI strategy roadmap — a prioritized, forward\-looking plan for how AI should evolve across the platform organization over the next one to three years
- Identify where AI creates step\-change opportunities in platform operations: what can be automated, augmented, or fundamentally re\-architected rather than incrementally improved
- Challenge existing platform assumptions — bring a clear\-eyed view of where current approaches should be replaced by AI\-native alternatives, not just enhanced by them
AI Enablement Across the Platform Organization
Drive activation and adoption of AI capabilities across all platforms EPEO operates:
- Partner with Platform Engineering, Developer Experience, Employee Experience, Foundational Platform, and Digital Core Platforms to move AI capabilities from available to embedded — integrated into how platforms are built, operated, and improved across the enterprise
- Serve as the strategic AI advisor across all platform organizations — providing a consistent, cross\-cutting AI lens that individual platform teams cannot maintain on their own
- Build and govern the AI portfolio framework for EPEO: how AI investments are categorized, evaluated for fit, measured for value, and reported to executive leadership
- Drive internal alignment across platform organizations on AI priorities, sequencing, and shared standards — operating with influence across teams that do not report directly into this role
- Develop internal AI platform fluency — bring platform engineering and SRE leadership along on the AI journey through structured evaluation frameworks and shared strategic context
AI Governance, Risk \& FinOps
Establish the governance and financial management layer for AI across the enterprise platform portfolio:
- Build and own the AI governance framework spanning all providers: model selection criteria, acceptable use policies, data classification standards for AI inputs, and enterprise risk guardrails
- Own AI FinOps as a strategic discipline:
+ Consumption visibility and cost attribution by platform, team, and use case
+ Model tier efficiency and cost\-per\-outcome tracking
+ Enterprise\-wide ROI reporting on AI investment across all platform providers
+ Budget governance and spend forecasting for AI at the EPEO portfolio level
- Ensure AI enablement activities meet enterprise standards for security, compliance, intellectual property protection, and responsible use
- Partner with CISO, Legal, and Compliance to maintain a risk management framework for AI that scales with adoption and adapts to evolving regulatory requirements
Vendor Strategy \& Provider Relationships
Lead EPEO’s strategic AI vendor relationships at the architecture and roadmap level:
- Engage leading AI and cloud platform providers as strategic partners — not just commercially but at the technical and roadmap level, influencing provider direction based on EPEO’s scale and enterprise needs
- Maintain a forward\-looking technology radar for AI: evaluate emerging models, agentic frameworks, and AI infrastructure patterns for enterprise applicability before they become mainstream
- Lead vendor capability assessments that go beyond marketing materials — forming independent, technically grounded views of where each provider’s AI platform is heading and what it means for EPEO’s business functions
- Manage the AI provider portfolio as a strategic asset: evaluate concentration risk, capability overlap, and investment sequencing across providers
Cross\-Functional Platform Partnership
Partner closely with platform organizations across EPEO to ensure AI strategy translates into embedded, operating reality:
- Platform Engineering — align AI enablement priorities with core infrastructure and platform delivery roadmaps, ensuring AI capabilities are built into the platform layer, not bolted on
- Developer Experience — collaborate on how AI changes the way engineers build, test, and ship software across the enterprise, identifying where AI augments the inner and outer loop
- Employee Experience — partner on how AI\-enabled platform capabilities reach and serve the broader workforce, extending value beyond engineering to all business functions
- Foundational Platform — work in close alignment on shared infrastructure, data, and runtime services that underpin AI workloads across EPEO’s portfolio
- Digital Core Platforms — engage on how AI strategy intersects with the core systems and business platforms that power enterprise operations, ensuring AI enablement spans both modern and foundational technology layers
- Partner with Security, Legal, and Compliance to ensure AI adoption across all platform organizations meets enterprise risk, data handling, and regulatory standards
Executive Alignment \& Strategic Communication
Build and maintain executive\-level alignment on AI platform strategy and investment:
- Present well\-formed AI strategy recommendations to senior and executive leadership — including business cases, opportunity cost of inaction, and multi\-year investment prioritization
- Provide clear executive visibility into EPEO’s AI platform health: adoption, governance posture, FinOps efficiency, and transformation progress
- Represent the AI platform strategy at internal leadership forums, external industry events, and in strategic vendor conversations
- Translate a fast\-moving AI landscape into clear, actionable direction for the platform organization — synthesizing vendor signals, market developments, and internal data into decisions leadership can act on
How success is achieved...* Maintaining a current, rigorous, and independent view of how AI is positioned across EPEO’s entire platform portfolio
- Identifying platform transformation opportunities ahead of the organization — forming views before consensus exists and bringing well\-formed recommendations to leadership
- Driving AI enablement through influence, not authority — building trust and alignment across platform organizations that do not report directly into this role
- Translating complex AI developments into clear, enterprise\-wide strategic direction that supports business functions and that leadership can act on with confidence
- Governing AI investments with the rigor of a FinOps discipline — not just tracking spend but connecting it to platform value and business outcomes
- Building and maintaining strategic vendor relationships that give EPEO early access to AI capabilities and influence over provider roadmaps
You'll have...AI Platform \& Technical Expertise* 12\+ years of experience in technology, including significant hands\-on platform strategy, enterprise architecture, or AI infrastructure leadership
- Deep working knowledge of the enterprise AI provider landscape across leading cloud platforms, enterprise AI assistant and productivity platforms, and frontier AI model providers — able to evaluate technical capabilities and architectural fit, not just vendor positioning
- Demonstrated experience assessing and rationalizing AI portfolios at enterprise scale — you have looked across a large organization’s AI landscape, identified what belongs and what doesn’t, and made recommendations that were acted on across business functions
- Hands\-on background in cloud platform architecture sufficient to evaluate AI capabilities at the architectural level — not just vendor positioning
- Experience designing or governing enterprise AI governance, FinOps, and enablement frameworks from the ground up — not inheriting and maintaining, but building and standing up
- Strong understanding of LLM architecture, agentic patterns, RAG, multi\-model orchestration, and AI infrastructure primitives at enterprise scale
Leadership Profile
- A strategically\-oriented leader who combines technical credibility with the ability to drive organizational change through influence
- Comfortable operating at both extremes:
+ Senior leadership strategy and investment discussions spanning technology, business functions, and enterprise priorities
+ Deep technical and architectural conversations with platform engineering and vendor engineering teams
- Proven ability to operate with a long time horizon in an environment where the technology changes quarterly — holding a multi\-year strategic view while remaining responsive to a rapidly evolving AI landscape
- Experience leading in large, matrixed engineering organizations (5,000\+ engineers) where influence without direct authority is the primary mode of getting things done
- Genuine intellectual curiosity about the AI frontier — you follow research, engage vendors at the technical level, form independent views, and are rarely surprised by what ships
- Track record of managing strategic AI vendor relationships with hyperscalers and AI\-native companies at the architecture and roadmap level
Enterprise Context
- Experience in a Fortune 500 or large\-scale enterprise environment with complex regulatory, compliance, and security requirements
- Demonstrated ability to build the business case for multi\-million dollar AI platform investments and present to C\-suite and board\-level stakeholders
- Background in cloud platform engineering, enterprise architecture, or a CTO\-adjacent strategy role strongly preferred — credibility in this role requires having built the platforms you are now strategizing about
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 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\.
\#LI\-Remote \#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 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 $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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.