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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
The Principal Architect, Data, AI, and Analytics leads a team of architects who guide, shape and influence the direction of the data, analytics, and AI that underly all of Ford's business. The Principal Architect works with senior leaders in Enterprise Technology and the Global Data & Analytics organization to ensure that Ford makes critical decisions based on a solid foundation of enterprise data.
This role will:
- Lead a team of more than 40 architects around the world who engage with teams who are building and managing systems across the Data, AI and Analytics platforms.
- Lead a cross-platform effort to align the design and implementation of data (design, transform, and storage), AI (Applied AI) and analytics (reporting, semantic layer, LLMs use of data catalog, and Knowledge Graph) technologies with overall technical strategy and industry trends.
- Lead a team that advises groups across the company on how to ensure that data, AI and Analytics systems are reliable, performant, and meet business needs.
- Lead a team that advises data engineering teams on domain driven design, data modeling (conceptual, logical, and physical), master data platform architectures, canonical modeling, and various enterprise data integration patterns
- Drive technical alignment across all regions and skill teams to appropriately align systems to industry and company standards.
- Collaborate with cloud and enterprise data center database infrastructure teams to build/configure the fit-for-purpose data platforms for functional use
- Have domain knowledge in Automotive or Manufacturing or Industrials would be considered ideal
- Collaborate with teams working on data, AI and analytics systems and related technologies to ensure that they are connected with the longer-term portfolio strategy and roadmap, and use judgement as to when these teams need to use existing, commonly deployed technologies and when to blaze a new path.
- Collaborate with cross-functional teams, including Infrastructure, developers, operations, and technical product managers, to gather requirements and ensure the platforms and systems meet the needs of internal and external stakeholders.
- Seamlessly integrate and define roadmaps for our strategic tool selection and our strategic systems roadmap.
Additionally, the scope of this role will:
- Influence leaders, architects, managers, and engineers across the company
- Lead by example regarding technical and software architecture by participating in design sessions and engineering projects
- Lead a team of architects located in Dearborn, Michigan and Chennai, India.
- Advise and mentor leaders and engineers on diverse topics including Data, AI and Analytics systems.
- Help the Global Data Insights & Analytics leadership drive a vision and roadmap for the future of our data, AI and analytics platforms
- Define and enforce architectural standards, ensuring compliance with industry standards, regulations, and security requirements
- Collaborate with the security team to ensure cloud-native applications and infrastructure meet cybersecurity standards
- Advise on how to prioritize updates of existing systems to meet the desired new architecture
- Stay on top of architecture trends and be prepared to advise teams across Ford how to keep up
- Ensure solutions are fully aligned to the Ford technology strategy
Minimum:
- Bachelor's degree or equivalent experience in a relevant field
- 15+ years of progressive responsibilities in IT or an IT focused business area
- 10+ years of experience building and architecting data intensive applications (data integration at scale, HA data platforms, managing various data technologies – relational, document, columnar, virtual warehousing, and architecting around various philosophies such as data mesh, data lake, and zero copy solutions)
- 10+ years of experience in a field related to Data, AI and Analytics systems
- Working knowledge of cloud computing, especially Google Cloud Platform
- Working knowledge of data, AI and analytics tools and technologies and how to apply it to real-world applications
Our preferred requirements
- Master’s degree or equivalent experience in a relevant field
- Extensive demonstrated competency in technical architecture
- Knowledge of “cloud native” concepts in software architecture and delivery
- Working understanding of basic cybersecurity principles
- Must be skilled at influencing leaders and workers across the company at many different levels
- Must be able to think and act strategically, without constraints
- Must be able to develop a team, including growing employees’ performance and maturity, recruiting, and retaining key talent.
- Strong demonstrated competencies influencing peers and business partners
- Strong communication and collaborative skills
- Ability to “thrive in ambiguity” in leading teams to select the right solutions amid a complex environment
- Proven ability to work closely with management one to four levels above and below both in the business and IT
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. Senior-level AI roles across all categories have a median of $228,700.
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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