Machine Learning Engineer Salary in Chicago 2026: Complete Salary Guide

Last verified: April 2026

Executive Summary

Machine Learning Engineers in Chicago command a competitive average salary of $150,220 annually, representing strong demand for AI and data science talent in the Midwest tech hub. Entry-level positions start around $96,570, while senior professionals with 10+ years of experience earn $219,696, reflecting the significant value companies place on advanced machine learning expertise. The salary range extends to $257,520 for top earners, indicating substantial earning potential for skilled professionals in this field.

Chicago’s tech ecosystem has grown substantially, with major companies, startups, and financial institutions increasingly investing in machine learning infrastructure and AI solutions. With a cost-of-living index of 107.3 (slightly above the national average), Chicago offers reasonable compensation relative to other major tech hubs like San Francisco or New York. This makes it an attractive market for machine learning engineers seeking competitive salaries without the extreme cost pressures of coastal tech centers.

Machine Learning Engineer Salary Data Table

Salary Level	Annual Compensation	Monthly Gross	Hourly Rate (est.)
Entry Level (0-2 years)	$96,570	$8,048	$46.40
Mid-Level (3-5 years)	$135,198	$11,267	$64.90
Experienced (6-10 years)	$180,264	$15,022	$86.70
Senior Level (10+ years)	$219,696	$18,308	$105.70
Average Salary	$150,220	$12,518	$72.20
Median Salary	$150,220	$12,518	$72.20
Top 10% Earners	$257,520	$21,460	$123.90

Salary Breakdown by Experience Level

Experience significantly impacts machine learning engineer compensation in Chicago. For further reading, explore these software engineering career guides. The progression from entry-level to senior positions shows a 127% salary increase, demonstrating the substantial return on experience investment in this field:

• 0-2 Years (Entry Level): $96,570 – Recent graduates and career changers typically start in this bracket, working on foundational ML projects and learning production systems.
• 3-5 Years (Early Mid-Career): $135,198 – Professionals at this stage demonstrate solid project ownership and technical depth, commanding a 40% increase from entry level.
• 6-10 Years (Experienced): $180,264 – Engineers with significant track record in deploying ML models earn substantially more, reflecting leadership capabilities and technical expertise.
• 10+ Years (Senior/Principal): $219,696 – The highest salaries reflect architects of ML systems, strategy contributors, and mentors who guide organizational machine learning direction.

Chicago vs. Other Major Tech Markets

Chicago’s machine learning engineer salaries compare favorably to other cities, offering competitive compensation with lower living costs than premier tech hubs:

City	Average ML Engineer Salary	Cost of Living Index	Salary-to-COL Ratio
San Francisco, CA	$185,000 (est.)	190.2	0.97
New York, NY	$175,500 (est.)	187.5	0.94
Chicago, IL	$150,220	107.3	1.40
Austin, TX	$148,300 (est.)	119.5	1.24
Denver, CO	$142,100 (est.)	115.2	1.23

Chicago’s salary-to-cost-of-living ratio of 1.40 is significantly higher than coastal tech centers, meaning engineers retain more purchasing power. While nominal salaries are lower than San Francisco, the actual quality of life and financial flexibility often exceeds that of higher-paying but much more expensive markets.

Five Key Factors Affecting Machine Learning Engineer Salaries in Chicago

1. Years of Professional Experience

Experience remains the strongest predictor of salary in machine learning roles. Engineers progress from foundational work at entry level to strategic system architecture at senior levels. The 127% salary increase from 0-2 years to 10+ years demonstrates how expertise in deep learning frameworks, model optimization, and production deployment directly correlates with compensation growth. Companies value engineers who have navigated real-world challenges and can mentor others.

2. Company Size and Industry Sector

Large enterprises, financial services firms, and well-funded tech startups pay substantially more than smaller companies or non-tech industries. Chicago’s strength in finance, insurance, and healthcare creates premium opportunities where machine learning optimization impacts millions in revenue. Fortune 500 companies headquartered in Chicago often pay above the city average, while early-stage startups may offer equity compensation to offset lower base salaries.

3. Technical Specialization and Skillset

Machine learning engineers specializing in computer vision, natural language processing, or reinforcement learning command higher salaries than general practitioners. Proficiency with specific tools and platforms—TensorFlow, PyTorch, cloud ML services, and data engineering—increases market value. Engineers combining ML expertise with software engineering best practices, production deployment knowledge, and system design see notably higher compensation packages.

4. Educational Background and Certifications

Advanced degrees (Master’s or PhD in computer science, statistics, or mathematics) often correlate with higher starting salaries and faster progression. However, self-taught engineers and bootcamp graduates can reach competitive compensation levels with strong portfolios and proven project results. Industry certifications in cloud platforms (AWS, GCP, Azure ML) and specialized credentials enhance earning potential.

5. Cost of Living Adjustments and Local Market Conditions

Chicago’s cost of living index of 107.3 influences salary negotiations and hiring budgets. While lower than San Francisco’s 190.2 or New York’s 187.5, it remains slightly above the national average. This modest premium supports Chicago’s ability to attract talent from lower-cost regions while remaining affordable compared to other major metros. Regional demand for AI talent in Midwest financial services and healthcare sectors sustains competitive salary pressure.

Historical Salary Trends for Machine Learning Engineers

Machine learning engineering has experienced explosive growth in Chicago over the past five years. In 2021, average ML engineer salaries in Chicago hovered around $128,000. The progression to $150,220 in 2026 represents a 17% increase over five years, outpacing general software engineer salary growth of approximately 12-14%. This acceleration reflects:

• 2021-2022: Initial surge in demand as companies pivoted to AI/ML strategies, raising entry barriers and salaries
• 2022-2023: Consolidation phase with continued growth despite some tech sector volatility
• 2023-2026: Renewed acceleration driven by generative AI and LLM adoption across industries
• 2026-2026: Stabilization at current levels with strong baseline demand and premium for specialized expertise

The growth trajectory suggests that machine learning engineering will remain among the highest-paying engineering specialties in Chicago, with particular strength in roles requiring production ML expertise and system design capabilities.

Expert Tips for Maximizing Machine Learning Engineer Salary in Chicago

1. Build Production-Ready Skills Beyond Jupyter Notebooks

Employers increasingly value engineers who understand the full ML lifecycle: data pipelines, model serving, monitoring, and maintenance. Develop expertise in containerization (Docker), orchestration (Kubernetes), and MLOps practices. Engineers comfortable deploying models to production and maintaining real-time systems command 15-25% salary premiums over those working exclusively on research and experimentation.

2. Develop T-Shaped Expertise with Software Engineering Depth

Combine deep machine learning knowledge with solid software engineering fundamentals. Master version control, testing, code review practices, and system design principles. This combination is rare and highly valuable—companies struggle to find engineers who bridge the ML-software engineering gap, creating significant salary leverage for those who possess both skill sets.

3. Target High-Impact Industries and Companies

Chicago’s financial services, healthcare, and insurance sectors offer above-average compensation for ML talent. Research companies like CME Group, Allstate, and emerging fintech firms that heavily invest in machine learning infrastructure. Within the tech sector, established companies with mature ML operations often pay more than seed-stage startups, though equity consideration may change this calculus.

4. Pursue Specialization in High-Demand Areas

Specializations in computer vision, time-series forecasting, recommender systems, or natural language processing fetch premium salaries. Consider the market demand in Chicago specifically—financial prediction models, healthcare diagnostics, and insurance risk assessment are high-value domains. Developing depth in these areas increases negotiating power.

5. Maintain Current Certifications and Public Portfolio

Keep technical certifications current (AWS ML, Google Cloud ML Engineer, Azure ML) and maintain an active GitHub portfolio with production-quality projects. Contribute to open-source ML projects and share technical writing. Engineers with visible expertise and community contribution often negotiate 10-20% higher salaries based on demonstrated capability.

Frequently Asked Questions About Machine Learning Engineer Salaries in Chicago

Q1: How does the machine learning engineer salary in Chicago compare to the national average?

Chicago’s average machine learning engineer salary of $150,220 is slightly above the national average of approximately $148,000, positioning it as a competitive market without the extreme compensation of coastal tech hubs. The advantage becomes more pronounced when considering cost of living—Chicago’s lower expenses mean greater real purchasing power for engineers compared to markets with nominally higher salaries.

Q2: What’s the typical salary progression path for machine learning engineers in Chicago?

A typical progression follows: Entry-level ($96,570) → 3-5 years ($135,198) → 6-10 years ($180,264) → Senior/Principal (10+ years, $219,696+). This represents approximately 40% increases between each progression level. Acceleration occurs when engineers take on system design responsibilities, mentor junior staff, and demonstrate business impact through their machine learning work. Title progression (Junior → Senior → Staff/Principal) often correlates with these salary jumps.

Q3: Do machine learning engineers in Chicago receive significant benefits and bonuses beyond base salary?

Yes, typical compensation packages include 15-25% performance bonuses, stock options (particularly at tech-backed companies), comprehensive health insurance, 401(k) matching (3-6%), professional development budgets, and flexible work arrangements. At top companies, total compensation can be 20-40% higher than base salary when bonuses and equity are included. Entry-level positions typically have smaller bonus structures (10-15%), while senior roles increasingly leverage equity compensation.

Q4: Are there significant salary differences between remote and in-office positions in Chicago?

Post-pandemic, many Chicago companies offer remote work flexibility for machine learning engineers. Full-time remote positions outside Chicago may pay 5-15% less due to location-based salary adjustments, though the difference is smaller than for non-tech roles. Chicago-based remote positions generally maintain local market rates. However, some companies explicitly adjust for location, so engineers accepting positions nominally based in Chicago but working remotely elsewhere should verify salary treatment—some companies apply cost-of-living adjustments downward.

Q5: What skills or certifications would most increase a machine learning engineer’s earning potential in Chicago?

High-impact skills include: MLOps and production ML systems, advanced proficiency with TensorFlow/PyTorch and cloud ML platforms (AWS SageMaker, Google Cloud AI), experience with large-scale data engineering, specialty domains like computer vision or NLP, and leadership/mentoring experience. Industry certifications (AWS Certified Machine Learning, Google Cloud Professional Data Engineer) provide 5-10% salary boosts. However, demonstrated project results and production impact matter more than certifications alone—build an impressive portfolio combining public repositories and case studies of real business impact.

Related Topics and Resources

• Data Science Engineer Salary in Chicago: Comparison and Career Path
• Software Engineer Salary in Chicago: 2026 Complete Breakdown
• AI/ML Jobs in Chicago: Companies Hiring and Compensation Analysis
• Machine Learning Engineer Salary by Company Size and Industry
• Cloud Engineer Salary vs Machine Learning Engineer: Which Pays More?

Data Sources and Methodology

This salary analysis is based on market data collected and estimated as of March 31, 2026. Please note: Data is estimated from a single source. While we strive for accuracy, salary figures may vary based on specific company, experience level, and individual qualifications. Always verify current compensation data with multiple sources and official resources before making career decisions or salary negotiations. Data was last refreshed April 5, 2026.

Salary ranges reflect typical full-time employment compensation including base salary. Benefits, bonuses, and equity compensation vary significantly by employer and are not included in these base figures. Cost of living index data is based on standard metropolitan statistical area (SMSA) calculations comparing housing, transportation, groceries, and services to the national baseline of 100.

Conclusion: Making Informed Career Decisions

Chicago offers solid career opportunities for machine learning engineers, with competitive salaries that provide strong purchasing power relative to other major tech markets. The $150,220 average salary coupled with a 107.3 cost-of-living index creates favorable conditions for building wealth and lifestyle flexibility compared to San Francisco or New York, where salaries are higher but living costs are dramatically elevated.

For career planning, understand that your earning trajectory depends heavily on deliberately building production ML expertise, expanding into complementary software engineering skills, and targeting high-value industries. Entry-level engineers should focus on mastering ML systems beyond models—learn deployment, monitoring, and scaling. Mid-career professionals should specialize and develop leadership capabilities. Senior engineers command premium compensation by architecting ML strategies and mentoring others.

Chicago’s growing AI and fintech sectors provide excellent venues for these skill developments. Research specific employers—CME Group, Allstate, Uptake, and numerous healthcare and insurance companies actively invest in machine learning talent. When negotiating offers, use this data as a reference while emphasizing your specific technical capabilities, project portfolio, and demonstrated business impact. Remember that while base salary matters, total compensation including bonuses, equity, and benefits often comprises 30-50% additional value.