Interested in this AI/ML Engineer role at Aculocity, LLC?
Apply Now →Skills & Technologies
About This Role
Aculocity AI Student Innovation Lab AI Innovation Associate
Aculocity AI Innovation Lab
Build what comes next in enterprise AI.
Remote. Paid. ~20 hrs./Week. Turn AI knowledge into real enterprise impact.
This is not a traditional internship.
It is a program that gives you hands-on experience designing the next generation of AI-enabled enterprises while working on real business problems, real systems and real outcomes.THE FUTURE STARTS HERE
Artificial Intelligence is reshaping industries, companies and careers at extraordinary speed. The question is no longer whether AI will transform the world. The question is who will build that future. At Aculocity, we believe the next generation of AI leaders should not wait until the next graduation to make an impact.
Through the Aculocity AI Innovation Lab, a select group of students will work alongside AI Engineers, Data Scientists, Software Engineers, Architects and Business Leaders to solve real-world challenges across manufacturing, commercial vehicles, engineering, supply chain, finance and digital operations.
As an Aculocity AI Innovation Associate, you will gain access to enterprise-scale AI initiatives, modern platforms, experienced mentors and opportunities that most students do not encounter until years into their careers.
ABOUT ACULOCITY
Aculocity is the AI, Data, Technology and Digital Transformation organization within the GVW Group, a global group of companies operating across commercial vehicles, manufacturing, engineering, supply chain, retail, financial services and industrial technologies.
Our mission is to help transform these businesses through artificial intelligence, modern data platforms, automation and digital innovation. Every day, we solve complex enterprise challenges that improve how vehicles are designed, built, sold, serviced and supported, while enabling smarter operations and better decision-making across the Group.
Why this matters
You will not be working on theoretical exercises. You will be contributing to initiatives connected to real business needs, real enterprise data environments and measurable operational impact.OUR INNOVATION PROGRAM
This associate program is designed for students who want more than a standard internship. We are looking for future builders, innovators and leaders who are ready to challenge themselves in a serious enterprise environment.
Only a small cohort of students will be selected to join the Innovation Lab. Successful associates will demonstrate technical ability, intellectual curiosity, strong communication skills and a passion for applying AI to create business value.
YOU SHOULD APPLY IF YOU WANT TO
- Work on AI that reaches beyond the classroom.
- Learn directly from experienced industry practitioners.
- Build a portfolio of meaningful, enterprise-scale AI work.
- Collaborate with multidisciplinary teams solving complex problems.
- Be part of a selective cohort focused on the future of applied AI.
WHAT YOU MIGHT HELP BUILD
Depending on project needs, your interests and your skills, you may contribute to initiatives such as:
- Building AI-powered applications using Large Language Models (LLMs).
- Developing intelligent AI agents and enterprise copilots.
- Applying machine learning to solve operational and business challenges.
- Designing proof-of-concepts using emerging AI technologies.
- Exploring Retrieval-Augmented Generation (RAG) and enterprise knowledge management.
- Working with real-world enterprise datasets to uncover insights and improve decision-making.
- Developing predictive, optimization and analytics solutions.
- Collaborating with multidisciplinary teams to deliver innovative AI solutions.
- Researching and evaluating emerging AI tools, frameworks and techniques.
WHY THIS EXPERIENCE IS DIFFERENT
Most student programs ask you to observe. This associate program asks you to contribute.
Most internships expose you to one team. This experience exposes you to real business problems.
Most academic projects stop at a prototype. Here, you will solve real business problems.
Most early-career AI experience is narrow. This associate program helps you understand how AI, data, software, business process and change management come together in the real world.
WHAT WE ARE LOOKING FOR
We are looking for exceptional students who are curious, innovative and passionate about applying technology to solve meaningful real-world problems.
- Currently enrolled in, or recently completed, an undergraduate or graduate program in Artificial Intelligence, Data Science, Computer Science, Software Engineering, Computer Engineering, Information Systems, Mathematics, Statistics or a related quantitative discipline.
- Strong academic performance and a demonstrated passion for learning.
- Strong technical problem-solving ability, with programming experience, Python is a plus.
- A solid foundation or strong interest in Agentic AI, Applied AI, Data Science, machine learning, analytics, algorithms or statistics.
- Strong analytical thinking and problem-solving skills.
- The ability to communicate effectively and collaborate within multidisciplinary teams.
- Curiosity, creativity and a desire to explore AI technologies.
- A genuine interest in applying AI to create measurable business value.
- Applicants must be legally authorized to work in the United States.
BENEFICIAL EXPERIENCE
Experience with any of the following would be advantageous, but is not required:
- Generative AI and Large Language Models
- Prompt engineering
- Machine learning frameworks
- SQL and data analytics
- Working with APIs, software integration, automation or enterprise platforms
- Microsoft Fabric, Power BI or modern analytics platforms
What matters most
Exceptional students come from diverse academic backgrounds. We are more interested in your ability to learn, innovate, collaborate and solve problems than your experience with any single technology.WHAT SUCCESSFUL ASSOCIATES TAKE AWAY
- Hands-on experience developing enterprise AI solutions.
- Mentorship from experienced AI Engineers, Data Scientists and business leaders.
- Exposure to enterprise-scale AI, data and digital transformation initiatives.
- Experience working with multidisciplinary teams on business-critical projects.
- Access to modern AI platforms, cloud technologies and enterprise data environments.
- A stronger professional portfolio built around meaningful AI projects.
- Professional networking opportunities across Aculocity and the GVW Group.
- A deeper understanding of how AI is transforming enterprises.
- Potential future employment opportunities following successful completion of the associate program.
PROGRAM DETAILS
Area
Details
Time commitment
Approximately 20 hours per week.
Work Location: Remote. Applicants must reside and perform their work in Alabama, Georgia, Idaho, Indiana, North Carolina, Ohio, or Tennessee.
Schedule
Flexible scheduling to accommodate academic commitments
Initial engagement
6 to 12 months, with the possibility of extension
Compensation
Hourly compensation
WHY JOIN THE INNOVATION LAB?
Artificial Intelligence is transforming every industry. While many organizations are exploring AI, few can offer students the chance to apply it across the breadth and complexity of a global industrial enterprise.
As an AI Innovation Associate, you will work where AI meets manufacturing, engineering, commercial vehicles, automation, supply chains, finance, enterprise operations and real-world decision-making.
You will collaborate with experienced professionals, tackle meaningful business challenges and help build practical AI solutions designed to deliver measurable impact.
Build what comes next
If you are passionate about solving complex problems, excited by emerging technologies and ready to help shape the future of enterprise AI, we invite you to apply to the Aculocity AI Student Innovation Lab. The future will be shaped by those who create it.
Job Duties Disclaimer:The duties and responsibilities outlined here are representative but not exhaustive of the tasks that the employee maybe requiredto perform. Management reserves the right to modify, add, or remove duties and to assign other tasks as necessary to meet business needs.
Equal Employment Opportunity:
The Company**is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, or any other characteristic protected by law.
Reasonable Accommodations (ADA Compliance):The Company complies with the Americans with Disabilities Act (ADA) and provides reasonable accommodations to qualified individuals with disabilities. Employees who require assistance or accommodation should contact Human Resources.
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 Aculocity, LLC, 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.
Aculocity, LLC AI Hiring
Aculocity, LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.