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Remote Full Stack Machine Learning Engineer Jobs in Chicago, IL

HHAeXchange

Sr. Full Stack Engineer

Mundelein, IL · Remote

$140K - $160K/yr

We're looking for a Sr. Full Stack Engineer who's eager to think big and build products that make a difference, improving the experience of tens of thousands of caregivers and families. You'll work ...

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All JobsFull Stack Machine Learning Engineer JobsRemote Full Stack Machine Learning Engineer JobsRemote Full Stack Machine Learning Engineer Jobs in Chicago, IL

Remote Full Stack Machine Learning Engineer information

See Chicago, IL salary details

$45.8K

$138.8K

$196.2K

How much do remote full stack machine learning engineer jobs pay per year?

As of Aug 19, 2026, the average yearly pay for remote full stack machine learning engineer in Chicago, IL is $138,833.00, according to ZipRecruiter salary data. Most workers in this role earn between $114,300.00 and $162,800.00 per year, depending on experience, location, and employer.

What is a remote full stack machine learning engineer?

A Remote Full Stack Machine Learning Engineer is a professional who designs, develops, and deploys machine learning solutions while working remotely. They handle both the front-end and back-end aspects of machine learning projects, including data preprocessing, model building, API development, and integration with user interfaces or cloud platforms. This role requires expertise in programming, machine learning frameworks, cloud services, and web technologies, allowing them to build end-to-end AI-driven applications from anywhere in the world.

What are the key skills and qualifications needed to thrive as a remote full stack machine learning engineer?

To thrive as a Remote Full Stack Machine Learning Engineer, you need proficiency in programming languages (such as Python or JavaScript), a solid understanding of machine learning algorithms, experience with web development frameworks, and typically a degree in computer science or a related field. Familiarity with tools like TensorFlow, PyTorch, Docker, cloud computing platforms (AWS, GCP), and version control systems (Git) is essential. Strong problem-solving skills, self-motivation, and clear communication are crucial soft skills, especially in remote and cross-functional team environments. These combined skills ensure effective design, deployment, and integration of machine learning solutions in scalable web applications while maintaining productivity in a remote setting.

What are some common challenges faced by remote full stack machine learning engineers, and how can they be addressed?

Remote Full Stack Machine Learning Engineers often encounter challenges such as managing effective collaboration with cross-functional teams and ensuring smooth deployment of machine learning models into production environments. To address these, it's important to establish clear communication channels, regularly participate in virtual stand-ups, and use collaborative platforms such as GitHub and Slack. Additionally, staying organized with version control and thorough documentation helps maintain project transparency and ensures seamless handoffs between backend and frontend development. Proactively seeking feedback and scheduling regular check-ins with team members can further enhance productivity and integration within the team.

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

AspectRemote Full Stack Machine Learning EngineerRemote Data Scientist
Primary FocusDeveloping end-to-end machine learning applications, including backend, frontend, and model deploymentAnalyzing data, creating models, and generating insights without necessarily building full applications
Skills RequiredProgramming (Python, JavaScript), ML frameworks, web development, deployment toolsStatistics, data analysis, visualization, Python/R, SQL
Work EnvironmentCollaborates with developers, data engineers, and product teams in tech-driven companiesWorks with data teams, analysts, and business units in various industries

While both roles involve working with data and machine learning, a Remote Full Stack Machine Learning Engineer builds complete applications with integrated ML models, whereas a Remote Data Scientist focuses on data analysis and model creation without necessarily developing full applications.

What are the most commonly searched types of Full Stack Machine Learning Engineer jobs in Chicago, IL?

The most popular types of Full Stack Machine Learning Engineer jobs in Chicago, IL are:

What are popular job titles related to Remote Full Stack Machine Learning Engineer jobs in Chicago, IL?

For Remote Full Stack Machine Learning Engineer jobs in Chicago, IL, the most frequently searched job titles are:

What job categories do people searching Remote Full Stack Machine Learning Engineer jobs in Chicago, IL look for?

The top searched job categories for Remote Full Stack Machine Learning Engineer jobs in Chicago, IL are:

What cities near Chicago, IL are hiring for Remote Full Stack Machine Learning Engineer jobs?

Cities near Chicago, IL with the most Remote Full Stack Machine Learning Engineer job openings:

Remote Full Stack Machine Learning Engineer jobs near you
Infographic showing various Remote Full Stack Machine Learning Engineer job openings in Chicago, IL as of August 2026, with employment types broken down into 1% As Needed, 77% Full Time, 21% Part Time, and 1% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $138,833 per year, or $66.7 per hour.

Senior Full-Stack Cloud Engineer (Remote)

Ontrac Solutions

Chicago, IL • Remote

$90 - $100/hr

Full-time

Posted 13 days ago

Job description

Role Summary

OnTrac is seeking an experienced Senior Full-Stack Cloud Engineer to support our digital platform initiatives. In this role, you will design, build, test, and deploy modern full-stack application features spanning a React frontend, a Node.js REST API, and a PostgreSQL database. Working alongside internal engineering resources and partner teams, you will also manage cloud deployments, infrastructure automation, and CI/CD pipelines on Google Cloud Platform (GCP) using Terraform and Docker.

Requirements & Qualifications
  • Google Cloud Certification: Active certification as a GCP Associate Cloud Engineer or Professional Cloud Developer (or equivalent demonstrated depth on Google Cloud).
  • Containerization: Hands-on experience with Docker for containerizing Node.js applications and managing containerized runtime environments.
  • Infrastructure as Code (IaC): Solid proficiency writing and managing Terraform scripts to provision GCP infrastructure.
  • CI/CD Expertise: Proven ability to configure and maintain automated build, test, and deployment pipelines using Google Cloud Build or GitHub Actions.
  • Full-Stack Proficiency:
    • Frontend: Strong experience building modern, responsive UI components in React directly from Figma designs.
    • Backend: Expertise designing, building, and securing RESTful APIs with Node.js.
    • Database: Deep knowledge of PostgreSQL, including relational data modeling, writing SQL queries, and managing schema migration scripts.
  • Testing Beyond Unit Level: Hands-on experience implementing integration and end-to-end (E2E) testing strategies (e.g., Cypress, Playwright, Supertest) alongside standard unit testing.
Key ResponsibilitiesApplication Engineering & Interface Development
  • Design and implement full-stack application features across the React frontend, Node.js backend, and PostgreSQL schema.
  • Translate design team mockups from Figma into clean, responsive, and reusable React components and views.
  • Build and maintain the REST API layer handling communication between frontend and backend services.
Database Design & Schema Management
  • Design and evolve the PostgreSQL relational data model to support new application capabilities.
  • Author SQL queries and write schema migration scripts to execute database updates safely across environments.
Testing & Quality Assurance
  • Write unit tests for new and modified code as a standard part of the software development lifecycle.
  • Build and maintain integration and end-to-end (E2E) tests to validate user journeys and system interactions beyond the unit level.
GCP Infrastructure & DevOps
  • Provision, deploy, and configure application components on Google Cloud Platformincluding runtime services, managed PostgreSQL (Cloud SQL), and Cloud Storageusing Terraform.
  • Set up, optimize, and maintain CI/CD pipelines covering automated builds, test execution, container image generation, and deployments.
Technical Alignment & Documentation
  • Participate in collaborative working sessions with OnTrac project teams and external partner stakeholders (such as SADA) to define requirements and establish technical approaches.
  • Author and maintain comprehensive technical documentation, including system architecture, API contracts (OpenAPI/Swagger), and data models.