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About This Role
Opportunity Information: Please note: We are unable to sponsor H1-B's or TN's now or in the future.
About the Role
This is an early-career role designed for a new or recent graduate who is excited to build a career in applied AI. You'll join our Data & Analytics team and work alongside experienced engineers and external delivery partners to help design, build, and support AI-powered solutions — including chat assistants, workflow automation, and integrations with our business systems.
You won't be expected to know everything on day one. We'll invest in your growth through mentorship, hands-on projects, code reviews, and structured onboarding. What matters most is a solid foundation in programming and AI concepts, genuine curiosity, and the drive to learn quickly. If you've built things in school, an internship, a bootcamp, or on your own — and you're eager to do it in a production environment with people who will help you level up — we'd like to hear from you.
What You'll Do
You'll start with well-scoped tasks and grow toward more independent work as you develop. Day to day, you will:
- Help translate product requirements and user stories into AI/ML solutions, with guidance from senior engineers and the Product Manager
- Contribute to building and testing AI chat assistants and automation tools for internal teams, in collaboration with senior engineers and external partners
- Assist in integrating AI solutions with existing databases, APIs, and business applications
- Help build, train, and evaluate machine-learning and LLM-based features, learning our production, quality, and security standards as you go
- Contribute to agentic and retrieval-augmented (RAG) workflows using frameworks such as LangChain or similar — beginning with guided tasks and building toward independence
- Support and help maintain existing AI agents, tools, and automations, including troubleshooting with support from the team
- Write clean, well-documented code and take part in code reviews to build strong engineering habits
- Help support and promote our internal AI chat platform (WPChat) across the organization
- Document what you build, and stay current with AI/ML developments — sharing what you learn with the team
- Collaborate across IT, data, and business teams, and partner with external vendors on delivery
How You'll Grow — What You'll Learn Here
These are skills you'll develop on the job with mentorship — not prerequisites. Over roughly your first 6–18 months, you can expect to gain hands-on, production experience with:
- Agent orchestration frameworks (LangChain, LangGraph, or AutoGen) and patterns such as ReAct and plan-and-execute, including memory and human-in-the-loop flows
- Retrieval-augmented generation (RAG) and NLP tooling — e.g., LlamaIndex, Haystack, PyTorch, scikit-learn
- Deploying AI solutions on cloud platforms (AWS, Azure, or GCP) and platform tools such as Vertex AI, Amazon Bedrock, or Azure AI Foundry
- Working with AI gateways (e.g., Portkey, Kong) and MCP servers that connect to enterprise systems (Oracle, Workday, Salesforce, ServiceNow, Jira)
- Building and integrating RPA workflows and connecting them to LLM decision layers
- MLOps practices for deploying and monitoring models, and building conversational AI across channels (web chat, and potentially voice, SMS, and email)
What We're Looking For
The essentials — this is what we actually require:
- A bachelor's degree in Computer Science, Engineering, Data Science, or a related field — or an equivalent combination of education and practical experience (including bootcamps or a strong self-taught portfolio). Recent and upcoming graduates are welcome.
- Foundational programming ability in Python, demonstrated through coursework, internships, academic or personal projects, bootcamp work, or open-source contributions
- A working understanding of core AI/ML and large language model (LLM) concepts — how they work and where they're used — from classes, projects, or self-study
- Hands-on exposure to at least one area relevant to the role: building something with an LLM or an AI framework (e.g., LangChain), a machine-learning project, an API integration, or a chatbot. A class, capstone, or personal project counts.
- Comfort with engineering fundamentals: version control (Git), reading documentation, and debugging
- Strong problem-solving skills, attention to detail, and a genuine eagerness to learn
- Clear communication and a collaborative, team-oriented mindset
Nice to Have (Bonus, Not Required)
Any of these will strengthen your application, but none are expected of an early-career candidate:
- Any hands-on exposure to LLM agent frameworks (LangChain, AutoGen, LangGraph, CrewAI, Flowise) or RAG tools (LlamaIndex, Haystack)
- Any experience with a cloud platform (AWS, Azure, GCP) or an AI platform tool (Vertex AI, Bedrock, Azure AI Foundry)
- Exposure to LibreChat, MLOps tooling, AI gateways (Portkey, Kong), or MCP servers
- Coursework or projects in NLP, chatbots, or conversational AI (including voice, SMS, email, or web chat)
- Any internship or project experience, including in web, eCommerce, or general software development
- Familiarity with data privacy and security basics
- Interest in or exposure to the wholesale distribution, manufacturing, CPG, or eCommerce industries
Work Arrangement
This is a full-time, on-site position at our Oak Brook, IL office. We've made it on-site on purpose: early in your career, working shoulder-to-shoulder with experienced engineers is one of the fastest ways to learn, get real-time feedback, and grow into a strong AI engineer. You'll be part of a team that's invested in your development.
Location Type: On-Site Pay Range: USD $83,000.00 - USD $111,000.00 /Yr.
Salary Context
This $83K-$111K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Worldpac, 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. This role's midpoint ($97K) sits 54% below the category median. Disclosed range: $83K to $111K.
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.
Worldpac AI Hiring
Worldpac has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Oak Brook, IL, US. Compensation range: $111K - $111K.
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.
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