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Machine Learning Infrastructure Engineer Jobs in Illinois

Apex Systems

Senior Machine Learning Engineer

Chicago, IL · On-site

$107K - $147K/yr

Implement and productionize final solutions via infrastructure-as-code pattern. Implement data ... respect to Machine Learning Engineering. Partner with data architecture, data governance, and ...

IMC

Hardware Machine Learning Engineer

Chicago, IL · On-site

$200K - $225K/yr

We build the hardware, the software, and the infrastructure, so when you hit a bottleneck, you can ... Understanding of machine learning fundamentals - neural network architectures, inference ...

IMC

Hardware Machine Learning Engineer

Chicago, IL · On-site

$200K - $225K/yr

We build the hardware, the software, and the infrastructure, so when you hit a bottleneck, you can ... Understanding of machine learning fundamentals - neural network architectures, inference ...

Bespoke Labs

Machine Learning Engineer

Chicago, IL · On-site

... and building the infrastructure that makes world-scale RL training possible. This is a high ... Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post ...

Bespoke Labs

Machine Learning Engineer

Joliet, IL · On-site

... and building the infrastructure that makes world-scale RL training possible. This is a high ... Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post ...

Bespoke Labs

Machine Learning Engineer

Dekalb, IL · On-site

... and building the infrastructure that makes world-scale RL training possible. This is a high ... Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post ...

Bespoke Labs

Machine Learning Engineer

Rockford, IL · On-site

... and building the infrastructure that makes world-scale RL training possible. This is a high ... Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post ...

Bespoke Labs

Machine Learning Engineer

Springfield, IL · On-site

... and building the infrastructure that makes world-scale RL training possible. This is a high ... Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post ...

Bespoke Labs

Machine Learning Engineer

Peoria, IL · On-site

... and building the infrastructure that makes world-scale RL training possible. This is a high ... Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post ...

Bespoke Labs

Machine Learning Engineer

Aurora, IL · On-site

... and building the infrastructure that makes world-scale RL training possible. This is a high ... Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post ...

The Hartford

AI Machine Learning Engineer

Chicago, IL · Hybrid

$100K - $151K/yr

The Hartford is seeking AI Machine Learning Engineer to build Machine Learning Operations (MLOps ... Familiarity building CICD pipelines using Jenkins or equivalent Exposure with IAC (Infrastructure ...

Career Renew

Senior Machine Learning Engineer

Chicago, IL · Remote

$165K - $225K/yr

Career Renew is recruiting for one of its clients a Senior Machine Learning Engineer - this is a ... infrastructure (AWS, GCP, or Azure) Knowledge of handling large scale image data, data version ...

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 ... Experience with IAC (Infrastructure as Code) including Cloud Formation, Terraform, or similar

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All JobsMachine Learning Infrastructure Engineer JobsMachine Learning Infrastructure Engineer Jobs in Illinois

Machine Learning Infrastructure Engineer information

See Illinois salary details

$45.1K

$123.1K

$176.4K

How much do machine learning infrastructure engineer jobs pay per year?

As of Jul 15, 2026, the average yearly pay for machine learning infrastructure engineer in Illinois is $123,130.00, according to ZipRecruiter salary data. Most workers in this role earn between $104,200.00 and $136,600.00 per year, depending on experience, location, and employer.

What are some common challenges faced by Machine Learning Infrastructure Engineers, and how can these be addressed on the job?

Machine Learning Infrastructure Engineers often face challenges such as ensuring infrastructure scalability, managing resource allocation, and maintaining system reliability while supporting rapid experimentation by data science teams. Balancing the needs for flexibility in research environments with production-grade stability requires a deep understanding of both engineering best practices and the unique requirements of machine learning workflows. Collaboration with data scientists, clear communication about infrastructure capabilities, and staying current with fast-evolving technologies are key strategies for success. Most companies encourage ongoing learning and provide opportunities to contribute to architecture decisions, which makes this a rewarding environment for problem-solvers and innovators.

What are the key skills and qualifications needed to thrive in the Machine Learning Infrastructure Engineer position, and why are they important?

To thrive as a Machine Learning Infrastructure Engineer, you need a strong background in computer science, cloud computing, distributed systems, and experience with machine learning frameworks, often supported by a degree in a related field. Familiarity with tools such as Docker, Kubernetes, Terraform, as well as cloud platforms like AWS, GCP, or Azure, and certifications in cloud or DevOps technologies are highly valued. Strong problem-solving abilities, effective communication, and collaboration skills help engineers work seamlessly with data scientists and cross-functional teams. These skills are essential to design, implement, and maintain robust, scalable infrastructure that enables efficient machine learning development and deployment.

What is a Machine Learning Infrastructure Engineer job?

A Machine Learning Infrastructure Engineer designs, builds, and maintains the systems that support the development and deployment of machine learning models. This includes managing data pipelines, optimizing model training and inference, and ensuring scalability and reliability in production environments. They work closely with data scientists, ML engineers, and DevOps teams to create efficient workflows and infrastructure. Key technologies often include cloud platforms, containerization, orchestration tools, and distributed computing frameworks.

What are popular job titles related to Machine Learning Infrastructure Engineer jobs in Illinois? For Machine Learning Infrastructure Engineer jobs in Illinois, the most frequently searched job titles are:
What job categories do people searching Machine Learning Infrastructure Engineer jobs in Illinois look for? The top searched job categories for Machine Learning Infrastructure Engineer jobs in Illinois are:
What cities in Illinois are hiring for Machine Learning Infrastructure Engineer jobs? Cities in Illinois with the most Machine Learning Infrastructure Engineer job openings:
Machine Learning Infrastructure Engineer jobs near you
Senior Software Engineer (Machine Learning)

Senior Software Engineer (Machine Learning)

Valor Equity Partners

Chicago, IL • On-site

$126K - $166K/yr

Full-time

Re-posted 26 days ago

Job description

About Valor:
Valor Equity Partners is a different kind of private investment firm. We pioneered the idea of operational growth. We work side-by-side, shoulder-to-shoulder, to help grow the operations of great companies solving the world's biggest problems. We invest in technology and technology-enabled companies that innovate and disrupt existing industries - from biosciences to transportation to food to health and wellness. We've had the honor of serving some of the world's greatest entrepreneurs and companies, including Tesla, SpaceX, Anduril, Eight Sleep, GoPuff, and others.
Our values are core to all we do. These values are excellence, humility, integrity, and responsibility.
Valor means that we:
  • Strive for excellence in everything we do;
  • Maintain our humility and mutual respect no matter what circumstances we encounter;
  • Insist upon the highest level of integrity in our interactions and in the logic of our investment process; and
  • Demonstrate responsibility and dedication to all of our constituents.

About the Team:
On the Valor Labs Team, we develop cutting edge machine learning models to derive proprietary investment insights and build software applications to augment the Firm's investment decision making process. As a small team of software engineers and data scientists with diverse backgrounds, we work collaboratively on wide-ranging problems to deliver high-impact products for the Firm.
About the Role:
As a Software Engineer on our data science and machine learning team, you will contribute directly to the development of high-impact products. Working together with data scientists, engineers, and stakeholders, you will translate complex project requirements into actionable technical solutions and work collaboratively to build, deploy, monitor, and maintain those solutions in production. Your technical expertise and commitment to excellence will help drive the adoption of best practices and ensure the highest level of rigor in everything we do.
About You:
  • B.S. in Computer Science or related field
  • 5+ years of experience developing production-ready software systems
    • Although not necessary, prior work experience in financial services is highly valued
  • Expertise in end-to-end machine learning operations: model deployment, monitoring, and retraining, supporting integration with production data pipelines and API services.
  • Proficient with Python, especially machine learning libraries like NumPy, Pandas, Scikit-Learn, and PyTorch
  • Proficient with SQL, including transactional (e.g., PostgreSQL) and analytical (e.g., BigQuery) databases
  • Professional experience with most, if not all, of the following:
    • Containerization (e.g., Kubernetes and Docker)
    • Data processing (e.g., Prefect, Airflow, and dbt)
    • Parallel processing (e.g., Ray, Dask, and Spark)
    • Cloud infrastructure (e.g., Google Cloud Platform)
    • Continuous integration/continuous deployment (e.g. GitHub Actions)
    • Infrastructure as code (e.g., Terraform)
    • Tools to support machine learning operations (e.g., MLFlow and DVC)
  • Humble, hard-working, and collaborative

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