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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 Data Engineering Intern for its headquarters in Auburn Hills, Michigan.
Your mission, roles and responsibilities
Mission
- Support the digital transformation of the Costing department through advanced analytics, automation, and AI‑powered solutions
- Development of AI applications in Palantir and external platforms
- Implementation of LLM‑based solutions and agent creation
- Design and execution of ETL processes
- Programming using Python and/or TypeScript
- Development of Machine Learning models (classification & forecasting)
- End‑to‑end application development, automation, and report creation
Key Responsibilities
- Build, maintain, and optimize data pipelines and ETL workflows
- Develop dashboards, reports, and analytical models to support Costing activities
- Automate internal processes and repetitive tasks
- Implement intelligent agents and LLM‑based functionalities
- Develop end‑to‑end applications integrating data, logic, and UI
- Ensure high data quality and proper documentation
- Collaborate with internal stakeholders, i.e., analysts, and managers to identify needs and deliver digital solutions
Your profile and competencies to succeed
Technical Skills (Required)
- Strong SQL
- Python and libraries
- Java
- TypeScript & React
- Palantir
- Understanding of data modeling, data pipelines, APIs, and cloud environments
- Prompt engineering and Agent creation
- ML experience is a plus
- (Nice to have: Power BI, Palantir Foundry, RPA, Power Apps)
Soft Skills
- Strong teamwork and collaboration
- Rigor and customer‑oriented mindset
- Positive attitude and autonomy
- Strong analytical & problem‑solving abilities
- Good communication skills to interact with buyers, analysts, and managers
- Curiosity, proactiveness, and ability to work independently
- Organized, reliable, and motivated to learn
Education
- Working toward degree in Computer Science/Engineering with Data Science, AI Engineering, or Business Analytics
- Minimum 2.8 GPA
- No previous experience required (experience is a plus)
Language Requirements
- Ability to communicate effectively in English in meetings, documentation, and daily work
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
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 9,653 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Forvia, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $177,825 based on 160 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 Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
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).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
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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