Interested in this AI/ML Engineer role at Ford Motor Company?
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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
We made history and now we work to transform the future – for our customers, our communities and our families. You'll see your work on the road every day, helping people move freely and pursue their dreams. At Ford, you can build more than vehicles. Come build what matters.
Do you believe data tells the real story? We do! Redefining mobility requires quality data, metrics and analytics, as well as insightful interpreters and analysts. That's where Global Data Insight & Analytics makes an impact. We advise leadership on business conditions, customer needs and the competitive landscape. With our support, key decision makers can act in meaningful, positive ways. Join us and use your data expertise and analytical skills to drive evidence-based, timely decision making.
Ford Motor Company's Global Data Insights & Analytics (GDI&A) department crafts innovative services to prepare smart vehicles for a smart world. Here in the data science division, we apply user experience principles to ensure our methods serve our end users. We are an advanced team operating at the forefront of artificial intelligence, designing not just interfaces, but intelligent systems, agentic workflows, and dynamic data experiences.
Our product designers oversee a portfolio of product teams, identifying patterns and use cases across the portfolio to better serve their end users. They apply a design thinking mindset, user research skills, and business strategy to align teams around a human-centric roadmap.
In this position...
For this role, we are looking for a highly technical designer to define and execute the future of our AI-driven Insight and Analytics products. You will collaborate closely with product managers, data scientists, AI engineers, and fellow designers. A hybrid skillset encompassing end-to-end product design, stakeholder management, compelling multimedia storytelling, and a technical AI toolkit is essential to success.
Research chops, strong leadership skills, and diversity of experience are all a plus!
Based in Dearborn, MI, this is a hybrid position with a required four-day or more onsite presence each week.Relocation assistance may be available for qualified candidates.
What you'll do...
- Design Strategy and Execution: Translate complex product requirements into intuitive product and service experiences for a suite of advanced analytics and AI-driven products. Collaborate with Product Managers to shape the roadmap, manage the design backlog, contribute UX and service design requirements to PRDs, and identify cross-team patterns, dependencies, handoffs, and failure points that should inform scalable product decisions.
- User Discovery & Empathy: Champion the user’s perspective throughout the product lifecycle. Conduct hands-on generative and evaluative research (contextual inquiry, user interviews, usability testing) to identify friction points in complex data workflows and translate those needs into intuitive AI features.
- End-to-end Product and Service Design: Transform user research into actionable artifacts, including journey maps, service blueprints, personas, ecosystem maps, and current-state and future-state workflows. Craft elegant, user-centric flows and high-fidelity interactive prototypes tailored for data-rich, AI-native environments, while connecting digital touchpoints, handoffs, people, processes, policies, and backend systems into cohesive product and service experiences.
- AI-native design and technical collaboration: Utilize a working knowledge of Python, AI agents, and modern LLM frameworks to design and prototype agent-enabled workflows that support recommendations, automation, model outputs, feedback loops, confidence indicators, approvals, exception handling, and human oversight. Partner closely with engineering, data science, and product management to bridge design intent with implementation and use Model Context Protocols (MCPs) to connect users with AI agents and internal data sources.
- Data Visualization & Interaction Design: Partner with engineering to "bring data to life." Design robust modular UI components and intuitive insights dashboards that allow users to easily interpret complex KPIs, interact dynamically with AI models, and take immediate action.
- Design Storytelling & Multimedia: Translate complex AI concepts into clear, engaging visual narratives. Design and produce compelling product demo videos, walkthroughs, and multimedia content to communicate design intent, user value, and product vision to both technical and business stakeholders.
- Cross-functional collaboration: Evangelize human-centered and service-centered design within agile teams and across connected product groups. Advocate for design decisions using research-backed insights, co-facilitate design thinking and service blueprinting sessions, and align product managers, data scientists, AI engineers, software engineers, and business stakeholders around shared outcomes, clear handoffs, and scalable service solutions.
- Data-Driven Iteration: Balance user needs (Desirability) with business objectives (Viability) and technical feasibility (Feasibility). Partner with Analytics to evaluate design success and iterate continuously on UI/UX features using usage data, NPS, and direct customer feedback.
- Ecosystem integration: Ensure designs integrate seamlessly within the broader digital and operational ecosystem, creating consistent, context-aware experiences across multiple user personas, analytics tools, AI agents, workflows, and service touchpoints.
Key Characteristics
- Empathy: Curious about how other people work, live, and think—especially when they are interacting with complex data and intelligent systems. You enjoy meeting the users of our products and stakeholders to uncover their hidden needs.
- Systems Thinking: Having the ability to see things holistically to design the best suitable solution, map out complex agentic workflows, and bring clarity out of an ambiguous, fast-paced technical environment.
- Balance: Cutting a path to production means weighing users’ needs (Desirability) with business requirements (Viability) and the realities of AI and engineering constraints (Feasibility).
You'll have...
- Bachelor’s degree or equivalent combination of relevant education and experience.
- 7+ years of experience in UX research, interaction design, service design, or product design for complex, data-heavy, enterprise, or B2B applications
- 5+ years of experience translating complex systems, workflows, and data into clear user experiences, including strong visual/UI design, data visualization, and interaction design
- 3+ years of service design capability, including experience mapping end-to-end journeys, creating service blueprints, framing holistic business and user problems, and connecting digital touchpoints, people, processes, policies, and backend systems into cohesive product/service experiences
- 3+ years of demonstrated ability to work across ambiguous, multi-team ecosystems by mapping current-state and future-state workflows, identifying handoffs and failure points, and aligning product, engineering, data science, and business stakeholders around scalable service solutions
- 6 months+ of AI-native design experience, including designing workflows with copilots, AI agents, recommendations, automation, model outputs, feedback loops, confidence indicators, approvals, exception handling, and human oversight.This may include: + Practical familiarity with prompt and workflow design, including structuring user inputs, agent responses, refinement paths, review steps, and decision-support experiences so AI-enabled tools produce understandable, trustworthy, and actionable outcomes + Working knowledge of Python and practical familiarity with modern AI frameworks, autonomous agents, or Model Context Protocols (MCPs) for prototyping and collaborating with technical teams + Demonstrated portfolio showcasing end-to-end product and service design, including service blueprints, journey maps, interaction flows, AI-native workflows, interactive prototypes, and solutions for complex data or agent-enabled environments.
Even better, you may have...
- Master’s degree in human-computer interaction, human factors, psychology, communications, computer science, or similar field of study.
- Passion for growing your skills, tackling challenging AI/data problems, and sharing your learnings with others
- Ability to build credibility, trust, and shared language with highly technical team members (data scientists, AI engineers) and senior leaders
- Flexibility to work on initiatives with unclear and rapidly changing requirements typical of bleeding-edge AI development
- Expertise with modern design and prototyping tools (e.g., Figma), with a willingness to prototype in code when necessary to prove out an AI interaction
- Experience with multimedia storytelling, such as designing and producing product demo videos, walkthroughs, and compelling visual narratives
- Experience with designing written documentation (tech writing) to explain complex concepts simply
- Prior member of a Lean/Agile product team
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 salary grade of 7 or 8 and ranges from $99,600- 192,900.
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/GSR
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 $99K-$192K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($146K) sits 31% below the category median. Disclosed range: $99K to $192K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Ford Motor Company AI Hiring
Ford Motor Company has 12 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Product Manager, AI Software Engineer. Positions span Dearborn, MI, Remote, US, Dearborn, MI, US. Compensation range: $166K - $281K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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