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Volunteer Junior Machine Learning Engineer Jobs in Illinois

Socket.dev

Machine Learning Engineer

Oakbrook Terrace, IL · On-site

$110 - $145/hr

We are seeking a Machine Learning Engineer (MLOps) to support the productionization of traditional machine learning models (e.g., propensity and segmentation models) while also building and ...

New

Gotion, Inc.

Machine Learning Engineer

Manteno, IL

Role Summary We are seeking a highly motivated Machine Learning Engineer with a strong background in model architecture design and algorithm development, ideally with experience in scientific domains ...

Qfanalytics

Machine Learning Engineer

Chicago, IL · On-site

$80 - $120/hr

Preferred Qualifications PhD in Mathematics, Engineering, Physics or related field; 4-8 years experience working in Machine Learning; Experience with deep learning frameworks like TensorFlow or ...

Moody's Corporation

Machine Learning Engineer

Chicago, IL

$95K - $138K/yr

Expertise in Python programming, including machine learning libraries such as NumPy, Pandas, and PyTorch * Experience with machine learning operations practices, including continuous integration and ...

New

Paylocity

Senior Machine Learning Engineer

Schaumburg, IL · On-site

$120K - $159K/yr

Senior Engineer Machine Learning Position Overview Paylocity is growing its Machine Learning Engineering organization! Our machine learning engineering team is responsible for developing ...

Gotion, Inc.

Machine Learning Engineer

Manteno, IL · On-site

Role Summary We are seeking a highly motivated Machine Learning Engineer with a strong background in model architecture design and algorithm development, ideally with experience in scientific domains ...

Capital One

Lead Machine Learning Engineer

Chicago, IL · On-site

$105K - $139K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You ...

Capital One

Lead Machine Learning Engineer

Chicago, IL · On-site +1

$105K - $139K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You ...

Socket.dev

Lead Machine Learning Engineer

Chicago, IL · On-site

$179 - $205/hr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You ...

New

Capital One

Lead Machine Learning Engineer

Chicago, IL · On-site

$105K - $139K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You ...

Capital One

Lead Machine Learning Engineer

Chicago, IL · On-site +1

$105K - $139K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You ...

The Hartford

Senior AI Machine Learning Engineer

Chicago, IL · Hybrid

$126K - $166K/yr

As a Senior Machine Learning Engineer , you will play a critical role in designing, building, and ... Work with junior engineers and peers to provide mentorship and thought leadership. Be comfortable ...

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Volunteer Junior Machine Learning Engineer information

What is a volunteer junior machine learning engineer?

Volunteer Junior Machine Learning Engineers are individuals who offer their time and skills, often without pay, to assist in machine learning projects. They typically have foundational knowledge in programming, data analysis, and machine learning concepts, and they work under the guidance of experienced engineers or data scientists. Their responsibilities may include data preprocessing, building and testing models, and supporting research or development efforts. These roles provide valuable hands-on experience and are often sought after by students or career changers looking to break into the field.

What types of projects and tasks can a volunteer junior machine learning engineer expect to work on, and how do these contribute to skill development?

As a Volunteer Junior Machine Learning Engineer, you will typically assist with data preparation, exploratory data analysis, and building or improving basic machine learning models under the supervision of more experienced engineers. You may also help with tasks such as cleaning datasets, implementing algorithms, and evaluating model performance. These projects are designed to provide hands-on experience and mentorship, helping you develop technical skills while learning collaborative workflows in a team setting. This role is a great opportunity to build your portfolio, gain real-world experience, and network within the machine learning community.

What are the key skills and qualifications needed to thrive as a volunteer junior machine learning engineer, and why are they important?

To thrive as a Volunteer Junior Machine Learning Engineer, you need a foundational understanding of programming (especially Python), mathematics, and basic machine learning concepts, often supported by coursework or online certifications. Familiarity with tools like TensorFlow, scikit-learn, Jupyter Notebooks, and version control systems like Git is usually expected. Curiosity, teamwork, and strong problem-solving skills help you learn quickly and contribute effectively in a collaborative environment. These skills and qualities ensure you can support real projects, continue developing your expertise, and add value even at an entry or volunteer level.

What is the difference between Volunteer Junior Machine Learning Engineer vs Volunteer Data Analyst?

AspectVolunteer Junior Machine Learning EngineerVolunteer Data Analyst
Required CredentialsBasic programming, introductory ML knowledge, possibly some courseworkData analysis skills, Excel, SQL, basic statistics
Work EnvironmentTech-focused projects, coding, model developmentData interpretation, reporting, visualization
Employer & Industry UsageTech companies, research projects, startupsNonprofits, research institutions, business analytics

The Volunteer Junior Machine Learning Engineer and Volunteer Data Analyst roles both involve working with data, but the ML engineer focuses on developing machine learning models and algorithms, requiring some programming and ML knowledge. The Data Analyst primarily interprets data through visualization and reporting, often using tools like Excel and SQL. Both roles are valuable in various industries, but the ML engineer role emphasizes technical model development, while the Data Analyst role centers on data interpretation and communication.

What are popular job titles related to Volunteer Junior Machine Learning Engineer jobs in Illinois?

For Volunteer Junior Machine Learning Engineer jobs in Illinois, the most frequently searched job titles are:

What job categories do people searching Volunteer Junior Machine Learning Engineer jobs in Illinois look for?

The top searched job categories for Volunteer Junior Machine Learning Engineer jobs in Illinois are:

What cities in Illinois are hiring for Volunteer Junior Machine Learning Engineer jobs?

Cities in Illinois with the most Volunteer Junior Machine Learning Engineer job openings:

Volunteer Junior Machine Learning Engineer jobs near you
Infographic showing various Volunteer Junior Machine Learning Engineer job openings in Illinois as of August 2026, with employment types broken down into 1% As Needed, 72% Full Time, 26% Part Time, and 1% Contract. Highlights an 83% Physical, 3% Hybrid, and 14% Remote job distribution.

Machine Learning Engineer

Oakbrook Terrace, IL • On-site

Darwill, Inc.
Marketing • 201 - 500 employees
Learn more about Darwill, Inc.external

Full-time

Posted 5 days ago

Job description

Overview
Darwill is a nationally recognized print and marketing communications firm based in the west suburbs of Chicago. As a premier provider of complex, data-driven marketing solutions, we help CMOs and marketing leaders drive measurable performance through advanced analytics, automation, and AI-powered insights.
We are seeking a Machine Learning Engineer (MLOps) to support the productionization of traditional machine learning models (e.g., propensity and segmentation models) while also building and maintaining the core data pipelines on Databricks that power our analytics and modeling platforms.
This role is intentionally scoped for a mid-level engineer: someone with enough experience to work independently and make sound engineering decisions, but who is still hands-on, execution-focused, and eager to grow. This is not an entry-level position, and it is not a principal or architect-level role.
Location
Chicago, IL area (Oak Brook / West Suburbs)
Hybrid work model with 1-2 days onsite per week required
Reports To
VP of Data Engineering & Data Science
Responsibilities / Essential Functions
Data Engineering & Platform Foundations
  • Design, build, and maintain ETL pipelines in Databricks using Spark and Delta Lake
  • Independently implement data transformations, joins, and aggregations across large, multi-source datasets
  • Build and maintain data validation and quality checks to ensure reliability of downstream analytics and ML workflows
  • Optimize Databricks jobs for performance, scalability, and cost efficiency
  • Write and maintain clear technical documentation for data pipelines and tables

ML Engineering & MLOps
  • Partner closely with Data Scientists to support traditional ML model development, including feature engineering, training, validation, and deployment
  • Productionize propensity, ranking, and segmentation models used in large-scale marketing campaigns
  • Build and maintain repeatable ML pipelines for training, batch scoring, and inference
  • Implement model versioning, experiment tracking, and reproducibility standards
  • Support model performance monitoring, drift detection, and retraining cycles

Deployment, Monitoring & Operations
  • Deploy data pipelines and ML workflows into production environments serving millions of records
  • Implement monitoring and alerting for data and ML pipelines
  • Support A/B testing and model performance evaluation in partnership with Data Science
  • Troubleshoot production issues independently and collaborate effectively when escalation is needed

GenAI (Secondary / Directional)
  • Contribute to GenAI initiatives as capacity allows
  • Stay informed on emerging AI technologies and tooling
  • (GenAI is not the primary focus of this role today.)

Required Qualifications
Experience
  • 3-6 years of professional experience in machine learning engineering, data engineering, or a closely related role
  • Experience working in production environments with minimal day-to-day supervision
  • Demonstrated ability to collaborate effectively with Data Scientists and translate models into production systems

Technical Skills (Must-Have)
Data Engineering & Platform
  • Apache Spark (PySpark, SparkSQL)
  • Databricks (ETL pipelines, workflows, Delta Lake)
  • Strong SQL skills (complex queries, joins, window functions, optimization)
  • Experience building and maintaining scalable data pipelines

Programming & Machine Learning
  • Python (pandas, numpy, scikit-learn; experience with XGBoost or LightGBM preferred)
  • Feature engineering and data preparation for ML models
  • Working knowledge of supervised learning models (classification, regression, ranking)

MLOps & Production
  • Experience deploying ML models into production
  • Model versioning and experiment tracking (e.g., MLflow or similar)
  • Monitoring data quality and model performance in production
  • Supporting retraining and validation workflows

Cloud & Tooling
  • Experience with a major cloud platform (Databrick, AWS)
  • Familiarity with workflow orchestration tools (Databricks Workflows or similar)

Preferred Qualifications (Nice-to-Have)
  • Experience with propensity modeling, customer segmentation, or marketing analytics
  • Exposure to CI/CD concepts for data and ML pipelines
  • Experience with Docker or containerized deployments
  • Exposure to GenAI, LLMs, or RAG-based systems
  • Master's degree in Computer Science, Statistics, or a related field
  • Seniority Level
    Associate
  • Industry
    • Marketing Services
  • Employment Type
    Full-time
  • Job Functions

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