Location: Hybrid – Minimum 3 days per week on‑site (Vernon Hills, IL)
Duration: Co‑Op Term (6–8 months) January 2027 - August 2027
Department: Automation & Emerging Technology
Reports To: Emerging Technologies Leader
Candidate Level: Bachelor's, Master's, or PhD‑track students
Salary Target Range: $28/hr-$30/hr
Position Overview
We are seeking a highly motivated Machine Learning & Applied AI Co‑Op Student to join our Automation & Emerging Technology team. This role is ideal for students who want hands‑on ownership of real‑world machine learning experiments in a fast‑moving, startup‑like environment within a large enterprise.
The co‑op will focus on applied machine learning, data‑driven experimentation, and model evaluation, with opportunities to explore Generative AI and large language models where they meaningfully support ML‑driven use cases. Rather than production maintenance or traditional automation work, this role emphasizes problem framing, experimentation, and measurable impact.
This position follows a hybrid work model, with a minimum of three (3) days per week on‑site at our Vernon Hills, IL office.
Key Responsibilities
Lead machine learning experiments end‑to‑end, including:
Develop and evaluate ML models using enterprise datasets for use cases such as:
Prediction and classification
Pattern detection and insight generation
Decision support and optimization
Apply sound experimental design and evaluation techniques, including:
Train/validation/test strategies
Baseline comparisons
Error analysis and model diagnostics
Use Databricks for data analysis, experimentation, and scalable ML workflows
Define and track success metrics, such as:
Model accuracy, precision/recall, and robustness
Latency, scalability, and cost considerations
Business relevance and usability
Explore applied AI techniques, including Generative AI and LLMs, where appropriate (e.g., summarization, knowledge retrieval, or hybrid ML + LLM solutions)
Document experiments, assumptions, results, and technical tradeoffs; present findings and demos to technical and business stakeholders
Apply Responsible AI and data governance practices, including data privacy, security, and bias awareness
Required Qualifications
Currently enrolled in a Bachelor's, Master's, or PhD‑track program in Computer Science, Data Science, Machine Learning, Statistics, or a related field
Ability to work on‑site in Vernon Hills, IL at least three days per week
Strong proficiency in Python
Solid understanding of core machine learning concepts, such as:
Supervised and unsupervised learning
Feature engineering
Model evaluation and validation
Experience with common ML/data libraries (e.g., pandas, NumPy, scikit‑learn, or similar)
Experience with AI Tools like Copilot, Copilot GitHub etc.
Ability to work independently, take initiative, and operate effectively in ambiguous problem spaces
Strong analytical thinking and communication skills
Preferred Qualifications
Hands‑on experience with end‑to‑end ML projects, including experimentation and evaluation
Familiarity with Databricks or similar data/ML platforms
Exposure to cloud‑based ML workflows (Azure preferred)
Experience with deep learning or NLP frameworks (e.g., PyTorch, TensorFlow, Hugging Face)
Working knowledge of Generative AI or LLMs as an applied technique (not required)
Prior internship, research, or applied ML project experience with measurable outcomes
What You'll Gain
Ownership of real machine learning experiments with direct business visibility
Experience working in a startup‑like, experiment‑driven environment inside a large enterprise
Hands‑on exposure to enterprise‑scale data and ML workflows using Databricks and Microsoft platforms
Mentorship from experienced AI and Emerging Technology leaders
Strong preparation for full‑time roles in Machine Learning Engineering, Applied Data Science, or AI Engineering
Rust-Oleum is an equal opportunity employer. Employment selection and related decisions are made without regard to sex, race, age, disability, religion, national origin, color, or any other protected class.
From Carboline's public posting, laid out for reading by newgrad.ai with the wording unchanged. Check the original before you apply.
