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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
Creating the future of smart mobility requires the highly intelligent use of data, metrics, and analytics. That’s where you can make an impact as part of our Global Data Insight \& Analytics (GDIA) team. We are the trusted advisers that enable Ford to clearly see 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.
Employees in this job function are responsible for designing, developing, testing and maintaining software applications and products to meet customer needs both on\-prem and cloud native. They are involved in the entire software development lifecycle including designing software architecture, writing code, testing for quality and deploying the software to meet customer requirements. Full\-stack software engineering roles, who can develop all components of software including user interface and server side also fall within this job function.
As an AI/ML Engineer, you will join the Product Team, collaborating closely with Product Managers, Product Designers, Data Scientists and fellow engineers to deliver impactful analytic solutions. In this role, you will take ownership of the full technical lifecycle, handling both the end\-to\-end development and the ongoing support and maintenance of these solutions.
You'll work across the full\-stack technologies to enable the highest priority work to be delivered. Within this highly collaborative environment, you will:
- Provide thought leadership across the greater Ford community
- Define, design, develop, and deploy applications/services
- Perform design review, code review and mentor junior team members
- Create proof\-of\-concepts to test business ideas with working software
- Use Agile principles and software craftmanship practices to sustainably and efficiently engineer software
- Collaborate with product managers, product designers, product owners, data scientists, and other software engineers to define product direction
- Integrate with other Ford systems and business processes to create new digital experiences
- Partner with software engineers and data scientists from other teams to build solutions with proven value
- 5\+ years' experience in Software Engineering.
- Bachelor’s degree in computer science, computer engineering or a combination of education and equivalent experience.
- 1\+ year experience with developing for and deploying to cloud platforms (e.g. GCP, PCF, Azure)
- Implement and optimize cloud services and tools (e.g. Terraform, BigQuery, GCP)
- Build and maintain the foundational systems that power scalable, reliable, high\-performance environments while also developing the intelligence layer that transforms data into actionable insights through machine learning. Develop internal frameworks, APIs, and developer tools using:
- Core Software Engineering:
- Languages \& Methodologies: Java, Python, SQL (or similar major programming languages), and Test\-Driven Development (TDD).
- Application Development: REST\-style microservices (Spring Boot, Cloud Run) and JavaScript\-based UI frameworks (Angular, React, or Vue).
- Cloud \& Infrastructure:
- GCP Architecture: Google Cloud Platform services including Cloud Run, Google Cloud Storage (GCS), Kubernetes Engine (GKE), Cloud SQL, Cloud IAM, and BigQuery.
- Infrastructure \& Operations: Infrastructure as Code (IaC), Terraform.
- DevOps, Reliability \& Security:
- CI/CD \& Containers: Jenkins, Tekton, GitHub Actions, Git/GitHub, Docker, Podman, Cloud Build and Deploy, and Artifact Registry.
- Monitoring: Splunk, Dynatrace, Grafana, Apigee, and Cloud Logging.
- DevSecOps: SonarQube, Fossa, Cycode, and Checkmarx.
- Data Engineering \& Machine Learning (MLOps):
- Data Processing \& Databases: Big Data, PySpark, MS SQL Server, PostgreSQL, and MySQL.
- ML Training: Google Vertex AI (Studio, Training, Model Registry), XGBoost, and CatBoost.
- Pipelines \& Workflows: Oozie workflows, Apache Airflow, and Astronomer.
- Production Integration: Vertex AI Endpoints, Batch Inference, and Astronomer.
Bachelor’s degree in computer science, computer engineering or a combination of education and equivalent experience.
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 salary grade 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 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\-GR1
Salary Context
This $99K-$192K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($146K) sits 33% below the category median. Disclosed range: $99K to $192K.
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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