Machine Learning Engineer

Job Description

Job Description

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

  Darwill’s Benefits & Perks At Darwill, we’re committed to supporting your health, financial well-being, and work-life balance with a comprehensive benefits package designed to support you and your family.

• 401(k) & Roth Retirement Plan – Build toward a secure financial future with retirement savings options and a competitive company match.
• Comprehensive Medical Insurance – Choose from medical plan options designed to meet your individual and family healthcare needs.
• Dental & Vision Insurance – Coverage to help support your overall health and well-being.
• Paid Time Off (PTO) – Take time to recharge, enjoy life, and spend time with the people who matter most.
• Health Savings Account (HSA) – Take advantage of tax-advantaged savings to help pay for eligible healthcare expenses.
• Flexible Spending Accounts (FSA) – Set aside pre-tax dollars to help manage eligible healthcare and dependent care expenses.
• Life Insurance – Darwill provides a $50,000 company-paid life insurance policy , with the option to purchase additional coverage.
• Short-Term & Long-Term Disability Insurance – Company-paid coverage that provides added financial protection when you need it most.
• Voluntary Supplemental Benefits – Additional 
• Hospital Indemnity and Accident Insurance coverage is available for employees who choose to purchase it.
• Health club Reimbursement - Darwill reimburses $30/month to gym of your choice if you attend 10 times or mo

Requirements: