Interested in this AI/ML Engineer role at Volkswagen?
Apply Now →About This Role
Worldwide, the Volkswagen Group has a long tradition of dramatic innovations. The Volkswagen Group with its headquarters in Wolfsburg, Germany is one of the world’s leading automobile manufacturers and the largest carmaker in Europe. The Group comprises twelve brands from seven European countries: Volkswagen Passenger Cars, Audi, SEAT, ŠKODA, Bentley, Bugatti, Lamborghini, Porsche, Ducati, Volkswagen Commercial Vehicles, Scania and MAN.
Brief Role Description
At the Innovation & Engineering Center California (IECC), we represent the Volkswagen Group in applied research and development. Located in the heart of Silicon Valley, we create bold new ideas for the Volkswagen, Audi, Bentley, Lamborghini, Bugatti and Porsche brands. We’re a team of engineers, designers, scientists, and psychologists looking to develop innovations for future generations of cars, and to transfer technologies from many industries and research institutions into the automotive domain. Our mission is to drive change which means we are not only impacting one of the world’s largest car makers, but also the lives of millions of people. Are you ready to join us?
Role Summary
This role will be tasked with applying machine learning/deep learning to the automotive industry. Applications could include autonomous driving, manufacturing, material design etc. Working in our facility requires a high level of independence and an ability to deal with lots of ambiguity. In addition to assigned projects, this individual and subsequent team will be responsible for coming up with the “next big thing” that nobody has thought of as yet, building it and selling it.
This position should be held by a Computer Science Engineer, or equivalent, with experience in the development of Software and Software architectures.
Possible Tasks within this Role
Software Development
- Maintains, as well as furthers, enhances existing machine learning modules.
- Designs and implements new machine learning based approaches based on existing frameworks.
- Keeps up to speed with the state of the art of academic research and technology in the industry.
- Transfers technologies and solutions to Volkswagen Group.
Software Testing and Evaluation
- Integrates new software modules into the framework.
- Tests developed modules in the prototype (unit tests, integration tests, in-vehicle/field tests, etc.).
- Analyzes and evaluates conducted tests.
Process Documentation
Develops technical specifications and documentation.
* Qualification requirements
Relevant Work Experience
5+ years related work experience or equivalent combination of education and related work experience)
Education
Required
Master’s Degree in Computer Science or equivalent.
Desired
PhD.
Skills
- Strong communication skills - ability to explain complex technical topics to non-experts.
- Team player with good collaboration and integration skills – joining people, processes and systems.
- Highly innovative and capable of seeing problems and potential solutions in a new light.
- Able to work in a dynamic, multi-disciplinary, time-line and target oriented work environment.
- Problem solving skills.
- Analytical skills.
- Conceptual thinking skills
#LI-CK1
Compensation Data
This position is located in Belmont, CA. The employer anticipates a pay range for this location between $145,000 - $195,000.
Competencies
Act as an owner Be a pioneer Handle complexity Live integrity & compliance Perform together Live passion Think ahead Volkswagen Group of America is an Equal Opportunity Employer. We welcome and encourage applicants from all backgrounds, and do not discriminate based on race, sex, age, disability, sexual orientation, national origin, religion, color, gender identity/expression, marital status, veteran status, or any other characteristics protected by applicable laws.
This role description is a guideline and does not create contractual rights between the Company and any of its applicants. The Company does not enter into any type of employment contract, implied or written, with its applicants regarding job security.
This Organization participates in E-Verify. We maintain a drug free workplace and perform pre-employment substance abuse testing.
Job ID: 31204 Company: Volkswagen Group of America, Inc. Location: Belmont, CA, US, 94002 Department: Research and Development Career Level: Professionals Working Model: Full-time Contract Type: Unlimited Remote Working: Up to 40% Posting Date: Oct 7, 2026 Join the New Volkswagen team.
If you aspire to be part of the mobility of the future, Volkswagen is for you. We are the ideal match for people with a passion for innovation, oriented to achieve sustainable results, and who can work in a team generating positive and collaborative work environments. Here is your next challenge.
Founded in 1955, Volkswagen of America, Inc., an operating unit of Volkswagen Group of America, Inc. (VWoA) is headquartered in Reston, Virginia. It is a subsidiary of Volkswagen AG, headquartered in Wolfsburg, Germany. VWoA’s operations in the United States include research and development, parts and vehicle processing, parts distribution centers, sales, marketing and service offices, financial service centers, and its state of-the art manufacturing facility in Chattanooga, Tennessee.
Learn more about us
About us | LinkedIn Volkswagen of America, Inc
About Us | LinkedIn VW Chattanooga Operations
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Support
For more information contact:
Chattanooga Operations Production: [email protected]
All Others: [email protected]
Salary Context
This $145K-$195K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Volkswagen, 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 in Demand for This Role
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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($170K) sits 20% below the category median. Disclosed range: $145K to $195K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Volkswagen AI Hiring
Volkswagen has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Belmont, CA, US. Compensation range: $195K - $195K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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