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Scientific Machine Learning Jobs in Illinois (NOW HIRING)

Currently enrolled in a Bachelor's, Master's, or PhDtrack program in Computer Science, Data Science, Machine Learning, Statistics, or a related field * Ability to work onsite in Vernon Hills, IL at ...

Bachelor's degree in Computer Science, Engineering, Data Science, Machine Learning, or equivalent practical experience. Required Qualifications * Solid hands-on experience with the GCP ecosystem ...

Bachelor's degree in Computer Science, Engineering, Data Science, Machine Learning, or equivalent practical experience. Required Qualifications * Solid hands-on experience with the GCP ecosystem ...

S. in Machine Learning, Computer Science, Electrical Engineering, Applied Mathematics, or a closely related field • Demonstrated expertise in model development, optimization, and algorithmic ...

Collaborate with battery scientists and domain experts to incorporate physical constraints or ... D. (preferred) or M.S. in Machine Learning, Computer Science, Electrical Engineering, Applied ...

MACHINE LEARNING ENGINEER (MLOPS / DATA ENGINEERING) Overview Darwill is a nationally recognized ... Partner closely with Data Scientists to support traditional ML model development, including feature ...

MACHINE LEARNING ENGINEER (MLOPS / DATA ENGINEERING) Overview Darwill is a nationally recognized ... Partner closely with Data Scientists to support traditional ML model development, including feature ...

Senior Machine Learning Engineer

Chicago, IL · On-site

$107.70K - $147.90K/yr

Machine Learning Engineer Must have: • 5+ years of implementing software product solutions in a ... This role will work cross-functionally with various data science teams, data engineering teams, and ...

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Scientific Machine Learning information

What are the key skills and qualifications needed to thrive as a Scientific Machine Learning professional, and why are they important?

To thrive as a Scientific Machine Learning professional, you need a strong background in mathematics, statistics, programming (often Python), and domain-specific scientific knowledge, typically with a graduate degree in a STEM field. Proficiency in machine learning frameworks (such as TensorFlow or PyTorch), scientific computing tools (like NumPy, SciPy), and experience with high-performance computing are commonly required. Critical thinking, problem-solving, and collaborative communication are vital soft skills for designing experiments and interpreting complex data. These skills ensure robust, reproducible results and the ability to bridge scientific inquiry with advanced computational methods.

What are some common challenges faced by professionals in Scientific Machine Learning, and how can they be addressed?

Professionals in Scientific Machine Learning often encounter challenges such as integrating domain-specific scientific knowledge with machine learning models, managing large and complex datasets, and ensuring that models are interpretable and physically consistent. Collaboration with domain experts and interdisciplinary teams is essential to bridge knowledge gaps and validate results. To address these challenges, it is helpful to invest time in understanding the underlying scientific principles, keep up-to-date with advancements in both machine learning and scientific fields, and utilize specialized tools and frameworks designed for scientific data.

What is scientific machine learning?

Scientific machine learning (SciML) is an interdisciplinary field that combines principles from machine learning and scientific computing to solve complex scientific and engineering problems. It involves developing algorithms and models that can learn from data and physical laws, such as differential equations, to make predictions, optimize systems, or gain insights into phenomena. SciML is widely used in areas like physics, biology, climate science, and engineering, enabling researchers to accelerate simulations and make data-driven discoveries. The field often leverages both traditional numerical methods and modern machine learning techniques, making it a rapidly evolving area of research.

What is the difference between Scientific Machine Learning vs Data Scientist?

AspectScientific Machine LearningData Scientist
Required credentialsAdvanced degrees in CS, ML, or related fields; knowledge of scientific computingDegree in CS, statistics, or related fields; strong analytical skills
Work environmentResearch labs, academia, industry R&D teamsBusiness analytics, tech companies, consulting firms
Industry usageResearch, scientific computing, engineering simulationsBusiness insights, predictive modeling, data analysis

Scientific Machine Learning focuses on integrating scientific knowledge with machine learning techniques for research and engineering applications. Data Scientists analyze data to extract insights and build predictive models for business or operational purposes. While both roles require strong technical skills, Scientific Machine Learning emphasizes scientific computing and domain-specific modeling, whereas Data Scientists focus on data analysis and visualization.

What job categories do people searching Scientific Machine Learning jobs in Illinois look for? The top searched job categories for Scientific Machine Learning jobs in Illinois are:
What cities in Illinois are hiring for Scientific Machine Learning jobs? Cities in Illinois with the most Scientific Machine Learning job openings:
Machine Learning Co-Op

Machine Learning Co-Op

Kop-Coat, Inc.

Vernon Hills, IL • On-site

$28 - $30/hr

Full-time

Posted 7 days ago


Job description

CoOp Student - Machine Learning & Applied AI

Location: Hybrid - Minimum 3 days per week onsite (Vernon Hills, IL)
Duration: CoOp Term (6-8 months)
Department: Automation & Emerging Technology
Reports To: Emerging Technologies Leader
Candidate Level: Bachelor's, Master's, or PhDtrack students


Position Overview

We are seeking a highly motivated Machine Learning & Applied AI CoOp Student to join our Automation & Emerging Technology team. This role is ideal for students who want handson ownership of realworld machine learning experiments in a fastmoving, startuplike environment within a large enterprise.

The coop will focus on applied machine learning, datadriven experimentation, and model evaluation, with opportunities to explore Generative AI and large language models where they meaningfully support MLdriven 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 onsite at our Vernon Hills, IL office.


Key Responsibilities

  • Lead machine learning experiments endtoend, 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

Required Qualifications

  • Currently enrolled in a Bachelor's, Master's, or PhDtrack program in Computer Science, Data Science, Machine Learning, Statistics, or a related field
  • Ability to work onsite 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, scikitlearn, 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

  • Handson experience with endtoend ML projects, including experimentation and evaluation
  • Familiarity with Databricks or similar data/ML platforms
  • Exposure to cloudbased 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 startuplike, experimentdriven environment inside a large enterprise
  • Handson exposure to enterprisescale data and ML workflows using Databricks and Microsoft platforms
  • Mentorship from experienced AI and Emerging Technology leaders
  • Strong preparation for fulltime roles in Machine Learning Engineering, Applied Data Science, or AI Engineering

Salary Target Range: $28/hr-$30/hr 

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.

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