ZipRecruiter

Log In

1

Machine Learning Engineer Jobs in Gurnee, IL (NOW HIRING)

Crate and Barrel

AI/ ML Engineer

Northbrook, IL · On-site

$100 - $125/hr

We are looking for an AI / Machine Learning Engineer with 2-5 years of experience to play a critical role in building and enhancing our production machine learning systems. You will be responsible ...

Varsity Tutors

Machine Learning Tutor

Highland Park, IL · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Schaumburg, IL · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Lake Forest, IL · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Buffalo Grove, IL · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Grainger Businesses

Data Engineer III

Lake Forest, IL · Hybrid

$104K - $174K/yr

This role reports to a Senior Manager of Machine Learning Engineering and partners closely with Inventory Reporting & Analysis (R&A). The objective of this role is to deliver high-quality, trusted ...

Grainger Businesses

Data Engineer III

Lake Forest, IL · On-site

$104K - $174K/yr

This role reports to a Senior Manager of Machine Learning Engineering and partners closely with Inventory Reporting & Analysis (R&A). The objective of this role is to deliver high-quality, trusted ...

Grainger Businesses

Data Engineer III

Lake Forest, IL · Hybrid

$104K - $174K/yr

This role reports to a Senior Manager of Machine Learning Engineering and partners closely with Inventory Reporting & Analysis (R&A). The objective of this role is to deliver high-quality, trusted ...

AbbVie

Engineer, Technology II

North Chicago, IL · On-site +1

Utilize AI tools, machine learning techniques, and modern software engineering methodologies to improve development efficiency, solution quality, and business value delivery. * Collaborate with ...

AbbVie

Engineer, Technology II

North Chicago, IL · On-site

$84K/yr

Utilize AI tools, machine learning techniques, and modern software engineering methodologies to improve development efficiency, solution quality, and business value delivery. * Collaborate with ...

previous pagenext page

Showing results 21-40

All JobsMachine Learning Engineer JobsMachine Learning Engineer Jobs in Gurnee, IL

Machine Learning Engineer information

See Gurnee, IL salary details

$30.2K

$123.6K

$185.7K

How much do machine learning engineer jobs pay per year?

As of Sep 9, 2026, the average yearly pay for machine learning engineer in Gurnee, IL is $123,595.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,400.00 and $148,800.00 per year, depending on experience, location, and employer.

What is a machine learning engineer?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

What does a machine learning engineer do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

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

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

What are some common challenges faced by machine learning engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

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

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

More about Machine Learning Engineer jobs

What cities near Gurnee, IL are hiring for Machine Learning Engineer jobs?

Cities near Gurnee, IL with the most Machine Learning Engineer job openings:

Machine Learning Engineer jobs near you
Infographic showing various Machine Learning Engineer job openings in Gurnee, IL as of August 2026, with employment types broken down into 1% As Needed, 71% Full Time, 25% Part Time, 1% Temporary, 1% Contract, and 1% Nights. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $123,595 per year, or $59.4 per hour.

Machine Learning Engineer, Frontier AI Evaluation (Contract)

Mundelein, IL • On-site

Cobalt
See more jobs at Cobaltexternal

Other

Posted 9 days ago

Key responsibilities

  • Produce written reasoning traces on real ML engineering tasks, diagnosing failures and explaining fixes.

  • Author ML engineering problems and task environments with automated success checks.

  • Evaluate model-generated ML code and configurations, ranking solutions and identifying points of failure.

Job description

About the role:

Cobalt is seeking machine learning engineers to produce the expert reasoning, task environments, and evaluation data used to train and assess frontier AI models on real ML engineering work.

This opportunity is suited to practitioners rather than only researchers: ML engineers, applied scientists, MLOps and platform engineers, and data engineers who have trained, deployed, and maintained models in production. A PhD is welcome but not required, and hands-on delivery experience counts for more here than publication record.

You do not need prior experience in data annotation or AI research. What matters is that you can diagnose why a pipeline or a training run is failing, decide what the right fix is, and explain both clearly enough for another engineer to follow.


What you'll do:

Depending on the project, you may:

  • Produce written reasoning traces on real ML engineering tasks, capturing how you diagnose a failing training run, a data pipeline defect, or a serving regression, including what you rule out and why
  • Author non-trivial ML engineering problems and task environments with checks that verify success automatically, including multi-file and multi-step tasks
  • Evaluate model-generated ML code and configurations, ranking solutions, explaining what makes the stronger one stronger, and identifying the point at which the approach goes wrong
  • Assess whether a proposed solution actually addresses the failure, and identify fixes that pass the immediate check but mask the underlying problem, degrade performance, or would not survive review
  • Design rubrics and partial-credit criteria for scoring multistep engineering tasks, and classify observed failures into a consistent taxonomy

Projects follow their own guidelines, formatting conventions, and quality standards, and you will work with feedback from reviewers and lab research teams.


Required qualifcations:

  • Several years of hands-on experience building, training, and deploying machine learning systems in production, with a track record you can point to
  • Strong coding ability in Python, plus working command of at least one deep learning framework such as PyTorch or JAX, and comfort reading unfamiliar codebases
  • Depth in at least one area, for example large-scale training and distributed compute, data pipelines and feature infrastructure, model serving and inference optimization, evaluation and monitoring, or fine-tuning and post-training workflows
  • Solid debugging discipline, including the ability to isolate a failure across data, model, and infrastructure rather than guessing at it
  • Ability to explain each step of your reasoning clearly in writing, and to produce work another engineer could reproduce and review


Why join Cobalt AI:

  • Advance frontier AI where it counts. Apply your expertise to data that frontier labs cannot obtain any other way, where your reasoning directly shapes how the next generation of models works through technical problems.
  • Grow professionally. Expand your influence through evaluation projects, advisory roles, and research collaborations, while developing a working understanding of how frontier models are trained and assessed.
  • Work with a top-tier network. Collaborate with researchers and engineers from leading institutions and labs on high-impact, flexible work.
  • Set your own schedule. Flexible 10 to 40 hour weeks that fit around your existing work and your life.
  • Competitive pay. Rates vary by project and are determined by a number of factors, including scope, skillset, and experience.

Apply