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Remote Embedded Machine Learning Jobs in Chicago, IL

Allied

Senior Machine Learning Engineer

Chicago, IL · On-site +1

$150K - $185K/yr

POSITION SUMMARY The Senior Machine Learning Engineer is responsible for designing, building, and ... Remote Here at Allied, we believe that great talent can thrive from anywhere. Our remote friendly ...

AppFolio

Sr. Machine Learning Engineer

Chicago, IL · Remote

$107.60K - $147.80K/yr

Assistant : a GenAI copilot embedded across the product experience * Flows: an agentic workflow ... Who we are looking for We're seeking a Sr Machine Learning Engineer to play a critical role in ...

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 fully remote role for US/Canada based candidates. Salary range: 165-225K USD yearly plus benefits plus ...

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Remote Embedded Machine Learning information

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$72.1K

$158K

$179.2K

How much do remote embedded machine learning jobs pay per year?

As of May 28, 2026, the average yearly pay for remote embedded machine learning in Chicago, IL is $158,007.00, according to ZipRecruiter salary data. Most workers in this role earn between $135,500.00 and $178,200.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Remote Embedded Machine Learning Engineer, and why are they important?

To thrive as a Remote Embedded Machine Learning Engineer, you need a solid background in embedded systems, machine learning algorithms, and programming languages like C/C++ and Python, often supported by a degree in computer science, electrical engineering, or related fields. Familiarity with microcontrollers, edge AI frameworks (such as TensorFlow Lite or Edge Impulse), and version control systems is typically required. Strong problem-solving skills, effective communication, and self-motivation are essential soft skills for collaborating remotely and troubleshooting complex issues. These skills ensure successful deployment of intelligent solutions on resource-constrained devices and effective teamwork in distributed environments.

What are some common challenges faced by Remote Embedded Machine Learning Engineers, and how can they be addressed?

Remote Embedded Machine Learning Engineers often encounter challenges related to hardware access, debugging embedded devices remotely, and collaborating with cross-functional teams across time zones. To address these, it's important to set up robust remote development environments, use simulation tools when physical hardware isn't available, and establish clear communication channels for effective teamwork. Regular virtual meetings and detailed documentation also help ensure alignment and smooth progress, despite the remote nature of the work.

What is a Remote Embedded Machine Learning Engineer?

A Remote Embedded Machine Learning Engineer is a professional who develops and deploys machine learning models on embedded systems like microcontrollers, IoT devices, and edge hardware, all while working remotely. Their work involves optimizing algorithms to run efficiently on devices with limited computing power, memory, and battery life. These engineers typically use frameworks such as TensorFlow Lite or TinyML to design intelligent features that operate directly on hardware, enabling real-time decision-making without relying heavily on cloud connectivity. They collaborate with cross-functional teams and often troubleshoot both software and hardware issues from a remote location.

What is the difference between Remote Embedded Machine Learning vs Remote Data Scientist?

AspectRemote Embedded Machine LearningRemote Data Scientist
Required CredentialsBachelor's or Master's in Computer Science, Electrical Engineering, or related fields; experience with embedded systems and ML frameworksBachelor's or Master's in Data Science, Statistics, or related fields; proficiency in data analysis and ML algorithms
Work EnvironmentEmbedded hardware devices, IoT systems, real-time processing environmentsCloud platforms, data analysis labs, remote offices
Employer & Industry UsageTech companies, IoT device manufacturers, automotive, roboticsFinance, healthcare, marketing, tech firms

Remote Embedded Machine Learning specialists focus on integrating ML models into embedded hardware for real-time applications, often working with IoT and robotics. In contrast, Remote Data Scientists analyze large datasets to extract insights, primarily working in cloud or office environments. Both roles require strong analytical skills but differ in technical focus and work settings.

What are the most commonly searched types of Embedded Machine Learning jobs in Chicago, IL? The most popular types of Embedded Machine Learning jobs in Chicago, IL are:
What are popular job titles related to Remote Embedded Machine Learning jobs in Chicago, IL? For Remote Embedded Machine Learning jobs in Chicago, IL, the most frequently searched job titles are:
What job categories do people searching Remote Embedded Machine Learning jobs in Chicago, IL look for? The top searched job categories for Remote Embedded Machine Learning jobs in Chicago, IL are:
Remote Embedded Machine Learning jobs near you

Senior Machine Learning Engineer

Allied

Chicago, IL • On-site, Remote

$150K - $185K/yr

Full-time

Medical, Dental, Vision, Life, PTO

Posted 26 days ago

Job description

POSITION SUMMARY
The Senior Machine Learning Engineer is responsible for designing, building, and deploying scalable machine learning systems that drive business impact. This role will partner closely with data scientists, AI Technical Product Owners, and engineering teams to integrate machine learning capabilities into real business processes. The emphasis is on operational excellence, scalability, and long-term maintainability rather than research and experimentation.
ESSENTIAL FUNCTIONS
  • Design and implement end to end machine learning pipelines that support data ingestion, feature generation, model training, validation, deployment, and monitoring.
  • Operationalize models in coordination with data scientists and ensure they run reliably with requisite alerts and monitoring in production environments.
  • Build reusable frameworks and patterns that reduce friction when deploying new models or updating existing ones.
  • Ensure pipelines are secure, auditable, and appropriate for use in regulated enterprise environments.
  • Own and evolve the MLOps toolchain that supports model versioning, artifact management, experiment tracking, and deployment workflows.
  • Implement continuous integration and deployment practices for machine learning systems.
  • Establish monitoring and alerting for model performance, data quality, drift, and system health.
  • Partner with cloud and platform teams to manage compute resources, cost controls, and environment configurations.
  • Work with application engineering teams to integrate machine learning outputs into downstream systems and user workflows.
  • Support real time and batch inference patterns depending on business needs.
  • Ensure that machine learning services meet performance, reliability, and availability expectations for production use.
  • Collaborate closely with data scientists to shape models that are production ready and operationally sustainable.
  • Provide guidance on feature engineering, model packaging, and performance tradeoffs from a deployment perspective.
  • Document standards, patterns, and best practices for building and operating machine learning systems.
  • Contribute to the maturation of the organization's overall AI and ML engineering discipline.
  • Other duties as assigned

EDUCATION
  • Bachelor's degree in Computer Science, Math, Statistics, or equivalent work experience required.

EXPERIENCE AND SKILLS:
  • 6+ years of strong experience building and operating machine learning systems in production environments.
  • Solid software engineering skills with Python and familiarity with modern ML frameworks such as PyTorch or TensorFlow.
  • Experience with data pipelines, workflow orchestration, and model deployment patterns.
  • Hands on experience with cloud platforms and managed ML services, with Azure, AWS, and/or Databricks experience preferred.
  • Understanding of MLOps concepts including model versioning, monitoring, testing, and lifecycle management.
  • Experience working with sensitive data in regulated industries such as healthcare or insurance is strongly preferred.
  • Ability to work cross functionally and translate between data science, engineering, and business stakeholders.

POSITION COMPETENCIES
  • Accountability
  • Analytical Problem Solving
  • Collaboration
  • Execution and Delivery
  • Quality and Risk Management
  • Systems Thinking
  • Technical/Functional Expertise

PHYSICAL DEMANDS
  • This is a standard desk role requiring extended sitting and computer work

WORK ENVIRONMENT
  • Remote

Here at Allied, we believe that great talent can thrive from anywhere. Our remote friendly culture offers flexibility and the comfort of working from home, while also ensuring you are set up for success. To support a smooth and efficient remote work experience, the internet connection must be obtained through a cable broadband or fiber optic internet service provider with speeds of at least 100Mbps download/25Mbps upload. Reliable internet service is essential for staying connected and productive.
The company has reviewed this job description to ensure that essential functions and basic duties have been included. It is not intended to be construed as an exhaustive list of all functions, responsibilities, skills, and abilities. Additional functions and requirements may be assigned by supervisors as deemed appropriate.
Compensation is not limited to base salary. Allied values our Total Rewards, and offers a competitive Benefit Package including, but not limited to, Medical, Dental, Vision, Life and Disability Insurance, Generous Paid Time Off, Tuition Reimbursement, EAP, and a Technology Stipend.
Allied reserves the right to amend, change, alter, and revise, pay ranges and benefits offerings at any time. All applicants acknowledge that by applying to the position you understand that the specific pay range is contingent upon meeting the qualification and requirements of the role, and for the successful completion of the interview selection and process. It is at the Company's discretion to determine what pay is provided to a candidate within the range associated with the role.

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