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Research Machine Learning Federated Learning Jobs in Chicago, IL

Ontrac Solutions

Machine Learning Engineer

Chicago, IL · On-site

$70 - $90/hr

Ontrac Solutions is seeking Machine Learning Engineers to support an urgent staff augmentation ... research-focused models. About Ontrac Solutions Ontrac Solutions is a strategic consulting and ...

Coin Flow

Machine Learning Engineer

Chicago, IL · On-site

$160K - $220K/yr

Coinflow is seeking a Machine Learning Engineer to help build the intelligence layer that powers our platform. This is a zero-to-one role. You will be the first dedicated ML hire and will own how ...

Coinflow

Machine Learning Engineer

Chicago, IL · On-site

$160K - $220K/yr

Coinflow is seeking a Machine Learning Engineer to help build the intelligence layer that powers our platform. This is a zero-to-one role. You will be the first dedicated ML hire and will own how ...

Kasmo Global

Machine Learning Engineer

Chicago, IL · On-site

Machine Learning Engineer Location: San Jose, CA/Chicago, IL Duration: 18 months contract with a possible extension What You'll Do • Redesign and optimize PayPal's MLOps and decision platform for ...

Darwill

Machine Learning Engineer

Villa Park, IL · Hybrid

MACHINE LEARNING ENGINEER (MLOPS / DATA ENGINEERING) Overview Darwill is a nationally recognized print and marketing communications firm based in the west suburbs of Chicago. As a premier provider of ...

Darwill, Inc.

Machine Learning Engineer

Villa Park, IL · On-site

MACHINE LEARNING ENGINEER (MLOPS / DATA ENGINEERING) Overview Darwill is a nationally recognized print and marketing communications firm based in the west suburbs of Chicago. As a premier provider of ...

Aquabyte

Machine Learning Engineer

Mundelein, IL

Aquabyte is seeking a Machine Learning Engineer to help develop and deploy new algorithms to fish farms across the world. You'll be responsible for software and machine learning model development of ...

PayPal

Machine Learning Engineer

Chicago, IL · On-site

This job will validate and develop machine learning models and algorithms to solve complex problems. You will work closely with senior engineers, data scientists, and product teams to enhance ...

PayPal

Machine Learning Engineer

Chicago, IL · On-site

This job will validate and develop machine learning models and algorithms to solve complex problems. You will work closely with senior engineers, data scientists, and product teams to enhance ...

PayPal, Inc.

Machine Learning Engineer

Chicago, IL · On-site

This job will validate and develop machine learning models and algorithms to solve complex problems. You will work closely with senior engineers, data scientists, and product teams to enhance ...

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All JobsResearch Machine Learning Federated Learning JobsResearch Machine Learning Federated Learning Jobs in Chicago, IL

Research Machine Learning Federated Learning information

See Chicago, IL salary details

$26.3K

$43.9K

$90.7K

How much do research machine learning federated learning jobs pay per year?

As of May 28, 2026, the average yearly pay for research machine learning federated learning in Chicago, IL is $43,867.00, according to ZipRecruiter salary data. Most workers in this role earn between $33,500.00 and $47,400.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Researcher in Machine Learning Federated Learning, and why are they important?

To thrive as a Researcher in Machine Learning Federated Learning, you need a strong background in computer science, mathematics, and machine learning, typically supported by a relevant advanced degree (e.g., PhD or MSc). Familiarity with Python, TensorFlow, PyTorch, and distributed computing frameworks, as well as knowledge of privacy-preserving techniques and relevant research publications, is essential. Excellent analytical thinking, problem-solving abilities, and clear scientific communication are key soft skills for success in collaborative research environments. These competencies are vital to drive innovation, rigorously evaluate federated learning approaches, and advance privacy-preserving AI technologies.

What are some common challenges faced when implementing federated learning in a research environment?

One of the primary challenges in research-focused federated learning roles is ensuring data privacy and security while maintaining model performance across distributed devices. Researchers must also address issues such as handling heterogeneous data sources, communication bottlenecks between nodes, and the complexity of debugging decentralized systems. Collaborating with cross-functional teams—such as data engineers, privacy experts, and domain specialists—is vital to overcome these hurdles and drive successful outcomes. Staying updated with the latest advancements and actively contributing to open-source initiatives can also help researchers address these evolving challenges.

What is a Researcher in Machine Learning Federated Learning?

A Researcher in Machine Learning Federated Learning is a professional who investigates and develops methods to train machine learning models across multiple decentralized devices or servers, while keeping data localized and private. Their work focuses on improving algorithms, ensuring data privacy, and addressing challenges related to distributed learning, communication efficiency, and model accuracy. They often collaborate with other researchers, publish findings, and contribute to advancing technologies that make it possible to use sensitive data for AI without compromising privacy.

What is the difference between Research Machine Learning Federated Learning vs Data Scientist?

AspectResearch Machine Learning Federated LearningData Scientist
CredentialsAdvanced degrees in CS, ML, or related fields; research experienceBachelor's or Master's in Data Science, Statistics, or related fields
Work EnvironmentResearch labs, academic institutions, tech companies focusing on privacy-preserving MLBusiness environments, analytics teams, data-driven departments
Industry UsageDeveloping federated algorithms, privacy-preserving ML modelsData analysis, modeling, reporting, and insights generation

Research Machine Learning Federated Learning specialists focus on developing privacy-preserving algorithms across distributed data sources, often in research or R&D settings. Data Scientists analyze and interpret data to inform business decisions. While both roles require strong ML knowledge, federated learning roles emphasize distributed systems and privacy, whereas Data Scientists focus on data analysis and visualization.

What are popular job titles related to Research Machine Learning Federated Learning jobs in Chicago, IL? For Research Machine Learning Federated Learning jobs in Chicago, IL, the most frequently searched job titles are:
What job categories do people searching Research Machine Learning Federated Learning jobs in Chicago, IL look for? The top searched job categories for Research Machine Learning Federated Learning jobs in Chicago, IL are:
What cities near Chicago, IL are hiring for Research Machine Learning Federated Learning jobs? Cities near Chicago, IL with the most Research Machine Learning Federated Learning job openings:
Research Machine Learning Federated Learning jobs near you
Infographic showing various Research Machine Learning Federated Learning job openings in Chicago, IL as of May 2026, with employment types broken down into 95% Full Time, 3% Part Time, and 2% Contract. Highlights an 7% Physical, 16% Hybrid, and 77% Remote job distribution, with an average salary of $43,867 per year, or $21.1 per hour.

Machine Learning Engineer

Ontrac Solutions

Chicago, IL • On-site

$70 - $90/hr

Contractor

Posted 29 days ago

Job description

Ontrac Solutions is seeking Machine Learning Engineers to support an urgent staff augmentation engagement for one of our clients.

This role is ideal for junior-to-mid-level engineers with strong Google Cloud Platform experience and a focus on building, maintaining, and supporting production-grade machine learning systems.

The selected engineers will work under the direct guidance of a Staff ML Architect and will focus heavily on daily MLOps execution, pipeline maintenance, model reliability, and production support for a high-traffic digital platform.

Required Credentials
  • 2+ years of experience in machine learning engineering, data engineering, software engineering, or a related technical role.
  • Hands-on experience supporting production or near-production ML systems.
  • Bachelor's degree in Computer Science, Engineering, Data Science, Machine Learning, or equivalent practical experience.
Required Qualifications
  • Solid hands-on experience with the GCP ecosystem, particularly Vertex AI components such as Workbench, Pipelines, and Model Registry.
  • Proficiency with modern ML frameworks, including PyTorch or similar technologies.
  • Experience with containerization tools, especially Docker, for automated builds and deployments.
  • Practical experience managing data processing workflows using Apache Spark and Airflow.
  • Understanding of MLOps best practices, including model deployment, monitoring, training workflows, inference support, and pipeline reliability.
  • Familiarity with real-time model serving and infrastructure tools such as Triton Inference Server and Terraform is highly preferred.
  • Strong problem-solving skills with the ability to troubleshoot, maintain, and optimize ML pipelines in a production environment.
  • Collaborative mindset with the ability to execute technical tasks reliably under the guidance of a senior architect.
Key Responsibilities
  • Support the design, deployment, monitoring, and maintenance of machine learning models in a high-traffic production environment.
  • Maintain, troubleshoot, and optimize end-to-end ML pipelines from raw data ingestion through offline and online model evaluation.
  • Execute daily MLOps tasks, including model training, inference support, pipeline monitoring, and deployment maintenance.
  • Work with tools such as GCP, Vertex AI, Spark, Airflow, Docker, PyTorch, and related MLOps technologies.
  • Build and manage automated containerized deployments to support continuous model operations.
  • Partner closely with the Staff ML Architect and other ML Engineers to ensure models are reliable, scalable, and production-ready.
  • Help identify and resolve performance, reliability, and scalability issues across ML workflows and infrastructure.
Preferred Qualifications
  • Prior experience supporting high-traffic digital platforms or consumer-facing products.
  • Experience with Triton Inference Server, Terraform, or similar infrastructure and real-time serving tools.
  • Experience working in staff augmentation, consulting, or fast-moving client-facing environments.
  • Strong interest in building reliable, production-grade ML systems rather than only experimental or research-focused models.
About Ontrac Solutions

Ontrac Solutions is a strategic consulting and technology solutions firm helping companies Innovate. Create. Elevate. through digital product consulting, cloud solutions, AI-based data solutions, and staff augmentation.

We partner with clients to bring the right technical expertise, execution support, and strategic guidance to complex business and technology initiatives.

Employment Type: CONTRACTOR

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