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About This Role
Transport is at the core of modern society. Imagine using your expertise to shape sustainable transport and infrastructure solutions for the future. If you seek to make a difference on a global scale, working with next\-gen technologies and the sharpest collaborative teams, then we could be a perfect match.
Who We Are
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The Complete Vehicle Features, Analysis, and Simulation Team resides within the Complete Vehicle (CV) organization at Volvo Group North America. Our mission is to be the Voice of the Customer and to build confidence that our products work for our customers through comprehensive verification and validation. CV‑FAST brings together feature ownership, engineering analysis, simulation, and data science to ensure vehicle features are robust, balanced, and verified and validated with the right methods at the right time. The team owns complete vehicle‑level feature performance, leveraging simulation, analytical data, physical testing, and digital verification strategies to front‑load learning, reduce late‑stage risk, and improve customer uptime. CV‑FAST works closely with Engineering, Test, Product Planning, Product Management, and Brands to enable data‑driven decisions across the full product lifecycle. Together with us, you will be part of a global and diverse team of highly skilled professionals. We have a strong culture based on our company values, which are central to our work.
We believe in a work environment where:
- We constantly strive for outstanding Performance.
- We are obsessed with Customer Success.
- We initiate Change to stay ahead.
- We willingly place our Trust in each other.
- We have a huge Passion for what we do.
What You will be doing
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At Volvo Group, you will contribute to the transformation of our company, the transport industry, and society at large. As a Data Science Co‑op within the CV‑FAST (Complete Vehicle Features, Analysis \& Simulation) team, you will help turn complex vehicle and engineering data into actionable insights that support data‑driven development across the product lifecycle. You will work with data generated from simulations, testing activities, and connected vehicles to support analysis, modeling, and decision‑making that improves product performance, efficiency, and customer value.
Working closely with engineers in Analysis \& Simulation, Feature Engineering, Testing, and Digital teams, you will contribute to building data pipelines, developing analytics and machine learning solutions, and enabling modern, digital workflows. You will support efforts to improve how we analyze, visualize, and leverage data—helping accelerate the shift toward simulation‑driven and data‑driven development practices across Complete Vehicle.
Responsibilities:
- Work with engineers and data scientists to analyze simulation, test, and vehicle data to identify trends, anomalies, and improvement opportunities
- Clean, transform, and structure raw datasets to make them usable for analysis and modeling
- Develop and maintain data pipelines, scripts, and notebooks to automate repetitive analysis tasks
- Create dashboards and visualizations to communicate key insights to engineering teams and stakeholders
- Use AI\-assisted tools to accelerate data exploration, generate code, and improve productivity
- Support the validation of data and models to ensure accuracy, robustness, and reproducibility
- Participate in team standups, technical discussions, and cross\-functional collaboration to align on priorities and deliverables
- Build predictive models using vehicle and test data to identify potential performance issues or trends
- Develop AI\-assisted tools or workflows (e.g., automated reports, insight generation, or data analysis pipelines) to improve engineering efficiency
- Analyze data from vehicle simulations and physical testing to support correlation and improve product validation strategies
- Support the development of digital twin or simulation\-driven analytics capabilities by ensuring high\-quality, structured data inputs
- Create or enhance Power BI or Streamlit applications that enable engineers to explore and interact with data more effectively
- Contribute to projects that automate or modernize engineering processes, reducing manual work and improving consistency
- Experiment with machine learning or generative AI techniques to improve how insights are generated and communicated
- Impact you will have:
- Help improve the speed and quality of engineering decisions through better data and insights
- Enable more efficient verification \& validation processes through data\-driven approaches
- Contribute to Volvo’s broader digitalization and AI transformation within product development
Who You Are
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You are a motivated and curious student with a strong interest in data science, analytics, and emerging AI technologies. You have a customer‑focused mindset and are eager to apply your technical skills to solve real‑world engineering problems. You are comfortable working with data, learning new tools, and collaborating with cross‑functional teams to generate insights that improve product decisions. You are energized by digitalization and excited to leverage data, simulation, machine learning, and AI to drive early learning, reduce risk, and support objective, data‑driven engineering decisions. You are interested in exploring how modern AI tools can accelerate workflows, enhance analysis, and enable more efficient and scalable engineering processes.
Required:
- Currently enrolled in a Bachelor’s or Master’s program in Data Science, Computer Science, Engineering, Statistics, or a related field
- Applicants must have a minimum cumulative grade point average of 2\.75
- Co\-ops are not enrolled in academic courses during their co\-op rotation and may work up to 40 hours per week
- Hands‑on experience with programming for data analysis (e.g., Python, SQL)
- Basic understanding of statistics, data analysis, and machine learning concepts
- Familiarity with AI/ML workflows, including data preparation, feature engineering, model training, and evaluation
- Exposure to Generative AI or AI\-assisted tools (e.g., using AI for coding, analysis, or automation)
- Experience working with datasets (cleaning, structuring, analyzing data)
- Strong problem‑solving skills and ability to communicate findings clearly
- Ability to validate outputs and ensure data quality, accuracy, and reproducibility
- Ability to work collaboratively in a team environment and learn in a fast‑paced setting
Preferrred:
- Experience with data visualization tools (e.g., Power BI, matplotlib, plotly)
- Exposure to machine learning libraries (e.g., scikit‑learn, TensorFlow, PyTorch)
- Familiarity with modern AI concepts (e.g., prompt engineering, embeddings, or retrieval‑based approaches)
- Experience building AI\-assisted workflows or lightweight tools (automation scripts, dashboards, or applications)
- Familiarity with data pipelines, automation, or scripting workflows
- Experience working with engineering, simulation, or test data
- Interest in automotive, transportation, or heavy‑duty vehicle systems
- Exposure to cloud platforms, databases, or big data tools
- Experience contributing to projects involving AI, analytics, automation, or dashboards
At the Volvo Group, we strive for a clear, transparent, and straightforward compensation approach, motivating you to contribute to the company’s growth. For all intern and co\-op positions, the hourly range is set at $17\.00 \- $46\.00\. The hourly rate for these roles is determined by several factors including, but not limited to, geographic location, academic classification, and degree seeking area of study. In addition to these factors, we believe in the importance of pay equity and consider internal equity of our current team members as part of any final offer.
We also offer the following benefits to interns and co\-ops:* Housing assistance, when applicable
- Countless career opportunities / internal mobility across our global organization
- Training and personal development
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, national origin, disability, or status as a protected veteran.
We value your data privacy and therefore do not accept applications via mail. Who we are and what we believe in
We are committed to shaping the future landscape of efficient, safe, and sustainable transport solutions. Fulfilling our mission creates countless career opportunities for talents across the group’s leading brands and entities.
Applying to this job offers you the opportunity to join Volvo Group. Every day, you will be working with some of the sharpest and most creative brains in our field to be able to leave our society in better shape for the next generation. We are passionate about what we do, and we thrive on teamwork. We are almost 100,000 people united around the world by a culture of care, inclusiveness, and empowerment.
Trucks Technology \& Industrial Division hire team players who are ready to create real customer impact. Our decentralized teams work close to our customers, with speed and autonomy, to build what they truly need.
Join us to collaborate on innovative, sustainable technologies that redefine how we design, build, and deliver value. Bring your curiosity, your expertise, and your collaborative energy, and together, we’ll turn bold ideas into tangible solutions for our customers and contribute to a more sustainable tomorrow.
Salary Context
This $35K-$95K range is in the lower quartile 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 Volvo Group, 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 ($65K) sits 63% below the category median. Disclosed range: $35K to $95K.
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.
Volvo Group AI Hiring
Volvo Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Greensboro, NC, US. Compensation range: $95K - $95K.
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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