JOB DESCRIPTION



Co-Op Student - Machine Learning & Applied AI




Location: Hybrid - Minimum 3 days per week on-site (Vernon Hills, IL)

Duration: Co-Op Term (6-8 months)

Department: Automation & Emerging Technology

Reports To: Emerging Technologies Leader

Candidate Level: Bachelor's, Master's, or PhD-track students



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:
  
  
  • Problem definition and hypothesis development
  
  
  • Data exploration and feature engineering
  
  
  • Model prototyping, training, and evaluation
  
  
  • Iteration based on quantitative results


• 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

• 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

• 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

• 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