Machine Learning Co-Op, Jan - Aug 27'
About The Position
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.
Requirements
- 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
Nice To Haves
- 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
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
Benefits
- 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
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What This Job Offers
Career Level
Entry Level
