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About This Role
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 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.
Enterprise Technology plays a critical part in shaping the future of mobility. If you’re looking for the chance to leverage advanced technology to redefine the transportation landscape, enhance the customer experience and improve people’s lives, this is the opportunity for you. Join us and challenge your IT expertise and analytical skills to help create vehicles that are as smart as you are.
The VSSE (Vehicle Software and Systems Engineering) team is advancing Ford’s use of artificial intelligence across vehicle engineering and product development. We are looking for a Full Stack Engineer who can use AI to accelerate software delivery across the full Software Development Lifecycle (SDLC), from requirements analysis and solution design to development, testing, deployment, monitoring, and continuous improvement.
In this role, you will design, build, and maintain secure and scalable web applications using Angular and cloud\-native services on Google Cloud Platform (GCP). You will apply AI\-assisted development practices to improve productivity, code quality, test coverage, documentation, and delivery speed while helping transform early\-stage AI prototypes into secure, production\-ready enterprise applications.
The ideal candidate is a hands\-on engineer with strong full stack development skills, practical experience using AI coding assistants and generative AI tools, and the ability to build modern, reliable applications that serve Ford engineering teams at scale.
1\. Full Stack Application Development
- Design, develop, test, and maintain full stack web applications using Angular, modern backend services, APIs, and cloud\-native technologies on GCP.
- Build responsive, intuitive, and performant user interfaces that simplify complex engineering workflows.
- Develop backend services, RESTful APIs, data integrations, and reusable components to support AI\-enabled applications.
- Integrate applications with enterprise data sources, authentication systems, engineering tools, and AI services.
- Ensure applications are secure, scalable, reliable, observable, and maintainable in production environments.
- Participate in architecture reviews, technical design discussions, code reviews, and production readiness assessments.
2\. AI\-Accelerated Software Development Across the SDLC
- Use AI coding assistants and generative AI tools to accelerate software development, refactoring, debugging, documentation, and code reviews.
- Apply AI to support requirements analysis, user story refinement, acceptance criteria generation, design exploration, and technical documentation.
- Leverage AI to generate and improve unit tests, integration tests, end\-to\-end tests, regression tests, and test data.
- Use AI\-assisted approaches for defect triage, log analysis, root\-cause investigation, and production support.
- Identify opportunities to automate repetitive SDLC activities and improve developer productivity.
- Promote responsible and secure use of AI tools while protecting Ford data, intellectual property, and enterprise standards.
3\. Cloud\-Native Engineering on GCP
- Build, deploy, and support applications using Google Cloud Platform services and cloud\-native architecture patterns.
- Work with GCP services such as Cloud Run, Cloud Functions, App Engine, GKE, Cloud Storage, Pub/Sub, Firestore, BigQuery, Cloud SQL, Secret Manager, Cloud Build, Artifact Registry, and Cloud Monitoring, as applicable.
- Support CI/CD pipelines, automated testing, containerization, infrastructure automation, and environment management.
- Collaborate with DevSecOps and platform teams to improve deployment reliability, scalability, performance, and observability.
- Apply cloud security best practices, including identity and access management, secrets management, network controls, and data protection.
4\. AI Tool and Platform Integration
- Help build and enhance AI\-enabled applications that support Ford Product Development teams across electrical, software, and vehicle systems domains.
- Integrate applications with LLMs, AI APIs, retrieval systems, vector databases, enterprise knowledge sources, and workflow automation tools.
- Contribute to reusable AI platform capabilities such as prompt templates, AI service wrappers, evaluation workflows, telemetry, and feedback loops.
- Support low\-code or self\-service AI capabilities that allow technical and non\-technical users to create guided workflows or AI\-assisted solutions.
- Explore and apply emerging AI development patterns, including retrieval\-augmented generation, agentic workflows, Model Context Protocol, and Agent\-to\-Agent integration where appropriate.
5\. Agile Collaboration and Delivery
- Work closely with product owners, designers, data scientists, software engineers, DevOps engineers, and business stakeholders.
- Translate user needs and product requirements into high\-quality technical solutions.
- Contribute to backlog refinement, sprint planning, estimation, demos, retrospectives, and delivery planning.
- Use metrics, telemetry, and user feedback to improve application performance, usability, adoption, and business impact.
- Share knowledge with the team on AI\-assisted engineering practices, reusable development patterns, and full stack best practices.
Minimum Qualifications:
- Bachelor’s Degree in Computer Science, Software Engineering, Engineering, Information Systems, or a related technical field.
- 7\+ years of professional software development experience.
- 7\+ years of experience building modern web applications using Angular, TypeScript, HTML, CSS/SCSS, and modern frontend engineering practices.
- Experience developing backend services, APIs, and integrations using one or more languages or frameworks such as Node.js, Java, Python, Spring Boot, NestJS, Express, or similar.
- Hands\-on experience with Google Cloud Platform or equivalent cloud platform.
- Practical experience using AI coding assistants, generative AI tools, or LLM\-based development workflows to accelerate software delivery.
- Understanding of how AI can support the SDLC, including requirements analysis, coding, testing, documentation, debugging, deployment, and operational support.
- Experience with REST APIs, authentication/authorization patterns, secure coding practices, and enterprise application integration.
- Experience with Git, CI/CD pipelines, automated testing, code reviews, and Agile software development practices.
- Ability to write clean, maintainable, well\-tested code and troubleshoot complex full stack issues.
- Strong communication and collaboration skills with the ability to work across technical and non\-technical teams.
Preferred Qualifications
- Experience deploying applications using GCP services such as Cloud Run, GKE, Cloud Functions, App Engine, Cloud Build, Artifact Registry, Cloud Storage, BigQuery, Pub/Sub, Firestore, Cloud SQL, Secret Manager, or Cloud Monitoring.
- Experience with Angular architecture patterns, RxJS, NgRx or other state management approaches, design systems, accessibility, and frontend performance optimization.
- Experience building AI\-enabled applications using LLM APIs, embeddings, vector search, retrieval\-augmented generation, prompt engineering, or agentic workflows.
- Experience integrating AI capabilities into internal tools, developer platforms, workflow automation, or engineering productivity applications.
- Experience with unit testing, component testing, API testing, and end\-to\-end testing using tools such as Jasmine, Karma, Jest, Cypress, Playwright, JUnit, PyTest, or similar.
- Experience with containerization and deployment tools such as Docker, Kubernetes, GKE, Terraform, or similar.
- Familiarity with observability tools, application monitoring, logging, tracing, and production support practices.
- Familiarity with Model Context Protocol, Agent\-to\-Agent communication, LangChain, Semantic Kernel, CrewAI, AutoGen, or similar AI orchestration technologies.
- Automotive, embedded systems, vehicle software, electrical architecture, hardware signals, or product development experience.
- Experience working in regulated or enterprise environments with strong security, privacy, data governance, and compliance requirements.
Desired Technical Skills
- Frontend: Angular, TypeScript, HTML, CSS/SCSS, RxJS, NgRx, Angular Material or similar component libraries
- Backend: Node.js, Java, Python, Spring Boot, NestJS, Express, REST APIs, microservices
- Cloud: Google Cloud Platform, Cloud Run, GKE, Cloud Functions, Cloud Storage, BigQuery, Pub/Sub, Cloud SQL, Firestore
- DevOps: Git, Docker, CI/CD, Cloud Build, Artifact Registry, Kubernetes, automated deployments
- AI/LLM: AI coding assistants, prompt engineering, LLM APIs, RAG, embeddings, vector databases, agentic workflows
- Engineering Practices: Agile, secure coding, API design, observability, documentation, production support
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 range of salary grades 7\-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 available for this position.
Domestic relocation 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. Candidates who are in commuting distance to a Ford hub location may be required to be onsite four or more days per week.
\#LI\-Hybrid
\#LI\-PW1
Salary Context
This $99K-$192K range is below the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($146K) sits 18% below the category median. Disclosed range: $99K to $192K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Ford Motor Company AI Hiring
Ford Motor Company has 4 open AI roles right now. They're hiring across Data Scientist, AI Software Engineer, AI/ML Engineer. Positions span Remote, US, Dearborn, MI, US. Compensation range: $192K - $250K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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