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About This Role
Forvia, a sustainable mobility technology leader
New trends and expectations are reshaping the automotive industry. Inspired by the exciting new challenges associated with this revolution, FORVIA anticipates the future of mobility developing cutting-edge solutions for smart life on board and sustainable mobility. If you’re willing to contribute and create value for tomorrow’s cleaner and smarter mobility, FORVIA is the place to be.
FORVIA is looking for a year-round AI Solutions Intern for its headquarters in Auburn Hills, Michigan.
Your mission, roles and responsibilities
The AI Solutions Intern supports business-focused AI transformation by translating operational challenges into well-defined use cases. Working with regional leaders and the Group Business Transformation Studio (BTS), the intern contributes to research, value-case development, requirements, pilots, adoption, and benefit measurement across engineering, manufacturing, quality, supply chain, and support functions. This role is designed for a master’s or PhD student seeking applied experience in AI, analytics, digital transformation, and manufacturing.
Internship Responsibilities:
Identify and assess AI use cases aligned with FORVIA’s business and manufacturing priorities.
Analyze data to establish baselines, estimate value and investment needs, and define KPIs.
Support AI demonstrations, workshops, and practical resources that build AI literacy and adoption.
Track pilot milestones, adoption, risks, user feedback, and measurable business outcomes.
Evaluate and test AI solutions with BTS; document requirements, acceptance criteria, lessons learned, and reusable practices.
Collaborate with engineering, plant, quality, supply chain, purchasing, and support teams to explore scalable AI-enabled improvements.
Contribute to change management and capability-building activities that support responsible AI adoption.
Comply with FORVIA requirements for ethics, quality, data privacy, cybersecurity, environmental responsibility, and responsible AI.
Internship Scope:
Own defined analyses or workstreams with coaching and stakeholder review. Prepare recommendations, requirements, success measures, and rollout considerations; final investment and deployment decisions remain with authorized business leaders.
Work within BTS, Group IT & Digital, data, cybersecurity, and architecture governance.
Knowledge / Skills:
Graduate-level knowledge of AI, machine learning, generative AI, data science, analytics, or digital transformation.
Strong analytical, research, structured problem-solving, communication, collaboration, and presentation skills.
Exposure through coursework, research, or projects to cloud platforms, data pipelines, systems integration, cybersecurity, or the AI solution lifecycle.
Interest in applying AI to manufacturing and engineering processes; prior industry experience is not required.
Ability to define metrics, evaluate results, and explain technical concepts and recommendations to nontechnical audiences.
Your profile and competencies to succeed
Education / Experience:
Currently enrolled in an accredited Master’s or PhD program in Engineering, Computer Science, Data Science, Artificial Intelligence, Information Systems, Operations Research, Business Analytics, or a related field.
Relevant graduate research, assistantships, internships, co-ops, capstone projects, hackathons, consulting projects, or prior employment preferred; five years of professional experience is not required.
Applied experience in machine learning, generative AI, data analytics, optimization, automation, digital products, or technology-enabled process improvement.
Experience on multidisciplinary teams preferred; automotive, industrial, or manufacturing exposure is an advantage.
Proficiency with relevant tools such as Python, R, SQL, data visualization, cloud services, or generative AI platforms is preferred.
Minimum 2.8 GPA
What we can do for you
- At Forvia, you will find an engaging and dynamic environment where you can contribute to the development of sustainable mobility leading technologies.
- We are the seventh-largest global automotive supplier, employing more than 157,000 people in more than 40 countries which makes a lot of opportunity for career development.
- We welcome energetic and agile people who can thrive in a fast-changing environment. People who share our strong values. Team players with a collaborative mindset and a passion to deliver high standards for our clients. Lifelong learners. High performers. Globally minded people who aspire to work in a transforming industry, where excellence, speed, and quality count.
- We cultivate a learning environment, dedicating tools and resources to ensure we remain at the forefront of mobility. Our people enjoy an average of more than 22 hours of online and in-person training within FORVIA University (five campuses around the world)
- We offer a multicultural environment that values diversity and international collaboration. We believe that diversity is a strength. To create an inclusive culture where all forms of diversity create real value for the company, we have adopted gender diversity targets and inclusion action plans.
- Achieving CO2 Net Zero as a pioneer of the automotive industry is a priority: In June 2022, Forvia became the first global automotive group to be certified with the new SBTI Net-Zero Standard (the most ambitious standard of SBTi), aligned with the ambition of the 2015 Paris Agreement of limiting global warming to 1.5°C. Three principles guide our action: use less, use better and use longer, with a focus on recyclability and circular economy.
Why join us
FORVIA, a global automotive technology supplier, comprises the complementary technology and industrial strengths of Faurecia and HELLA.
With over 137 500 people, including more than 12,000 R&D engineers across 40+ countries, FORVIA provides a unique and comprehensive approach to the automotive challenges of today and tomorrow. Composed of 6 business groups and a strong IP portfolio of over 12,400 patents, FORVIA is focused on becoming the preferred innovation and integration partner for OEMs worldwide.
In 2025, the Group achieved a consolidated revenue of 26.2 billion euros prior to IFRS 5. FORVIA SE is listed on the Euronext Paris market under the FRVIA mnemonic code and is a component of the SBF 120 index.
FORVIA aims to be a change maker committed to foreseeing and making the mobility transformation happen. www.forvia.com
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 Forvia, 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. Entry-level AI roles across all categories have a median of $110,408.
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.
Forvia AI Hiring
Forvia has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer. Based in Auburn Hills, MI, US.
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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