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Remote Embedded Machine Learning Jobs in Illinois

MNF Global

Remote Patent Attorney or Patent Agent

Chicago, IL · On-site +1

$170K - $300K/yr

... Remote Are you a passionate and skilled Patent Professional looking to elevate your career? We are ... Machine Learning, e-Commerce, user interfaces, medical devices, batteries, optics, and materials ...

micro1 AI

Engineering AI Evaluator (PhD)

Chicago, IL · Remote

$80 - $90/hr

Remote micro1 is engaging PhD-level Engineers in Electrical, Mechanical, or Chemical disciplines to ... Experience with or interest in AI, machine learning, or technology-driven projects (a plus, not ...

micro1 AI

Domain Expert - (STEM PhD)

Chicago, IL · Remote

$80 - $90/hr

Remote micro1 is engaging PhD-level Engineers in Electrical, Mechanical, or Chemical disciplines to ... Experience with or interest in AI, machine learning, or technology-driven projects (a plus, not ...

Tempus

Manager, Regulatory Submissions (Remote)

Chicago, IL · On-site +1

$110K - $150K/yr

Machine learning validation requirements * Lifecycle management of medical devices/IVDs * Changes ... remote $110,000-$150,000 The expected salary range above is applicable if the role is performed ...

OneStream

AI Product Specialist

Chicago, IL · Remote

Our AI solutions incorporate AI and machine learning spectrum, including (but not limited to) time ... OneStream is an Equal Opportunity Employer. #LI-REMOTE #LI-JP1

OneStream

AI Engineer

Chicago, IL · Remote

Our AI solutions incorporate applications across the AI and machine learning spectrum, including ... OneStream is an Equal Opportunity Employer. #LI-REMOTE #LI-JP1

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All JobsEmbedded Machine Learning JobsRemote Embedded Machine Learning JobsRemote Embedded Machine Learning Jobs in Illinois

Remote Embedded Machine Learning information

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 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 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 are the most commonly searched types of Embedded Machine Learning jobs in Illinois? The most popular types of Embedded Machine Learning jobs in Illinois are:
What are popular job titles related to Remote Embedded Machine Learning jobs in Illinois? For Remote Embedded Machine Learning jobs in Illinois, the most frequently searched job titles are:
What job categories do people searching Remote Embedded Machine Learning jobs in Illinois look for? The top searched job categories for Remote Embedded Machine Learning jobs in Illinois are:
What cities in Illinois are hiring for Remote Embedded Machine Learning jobs? Cities in Illinois with the most Remote Embedded Machine Learning job openings:
Remote Embedded Machine Learning jobs near you
Director, Ai Architecture & Platforms - National Office (remote)

Director, Ai Architecture & Platforms - National Office (remote)

YMCA

Chicago, IL • On-site, Remote

$144K - $177K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 8 days ago

YMCA rating

6.1

Company rating: 6.1 out of 10

Based on 1,982 frontline employees who took The Breakroom Quiz

506th of 710 rated non-profit organizations

Job description

x
Job Description
The Director of AI Architecture & Platforms is responsible for defining, implementing, and governing the organization's enterprise AI architecture and technical strategy. This role provides leadership for the design and delivery of scalable, secure, and compliant AI platforms and solutions that enable responsible adoption of generative AI, machine learning, and advanced analytics across the organization.
Reporting to senior IT leadership, the Director establishes architectural standards and reference designs for AI solutions, with a strong emphasis on cloud-native implementation using Amazon Web Services (AWS) and managed AI services such as Amazon Bedrock. The role ensures consistent, reusable, and secure patterns for integrating leading large language models (LLMs), including Anthropic, OpenAI, and Microsoft Copilot, into enterprise applications and workflows.
The Director partners closely with product, application development, data, security, legal, and business leaders to identify, prioritize, and design high-value AI use cases. This role ensures AI solutions are developed with a strong focus on business outcomes, operational readiness, and lifecycle management-from experimentation and prototyping through production deployment and ongoing optimization.
A critical responsibility of the role is establishing and enforcing AI governance frameworks, including policies, controls, and guardrails related to model selection, data usage, risk management, explainability, monitoring, and regulatory compliance. The Director also leads AI-specific cybersecurity and risk considerations, ensuring protections against data leakage, prompt injection, model misuse, and emerging AI threat vectors.
Through technical leadership, architectural rigor, and cross-functional collaboration, the Director enables the organization to adopt AI responsibly, securely, and at scale, while accelerating innovation and delivering measurable business value.
YMCA of the USA (Y-USA) embraces a remote-first working environment which means most employees work remotely from a home office within the continental United States.
We offer a full benefits package including medical, dental, vision, defined benefit plan (retirement savings), defined contribution plan (403(b) plan, life and disability insurances, technology stipend, and generous paid time off, all in a work from anywhere in the continental U.S. workplace.
Qualifications
A successful candidate will possess a majority of the following professional and personal attributes and competencies:
  • Bachelor's degree or higher required in a relevant field such as Business, or related disciplines required.
  • Advanced degree strongly preferred.
  • 8-12 years of experience required
  • Expert functional skills, advanced industry knowledge and experience, advanced managerial skills
  • Manages multiple teams or functions
  • Strategic and operational decision-making authority
  • Highly complex tasks and managerial tasks
  • Minimal supervision

Essential Functions
  • Define and lead the enterprise AI architecture strategy, ensuring scalable, secure, and compliant adoption of generative AI and advanced analytics across the organization.
  • Design and maintain reference architectures, standards, and patterns for AI solutions, including LLM-enabled applications and platform integrations.
  • Provide architectural leadership for AI platforms built on Amazon Web Services (AWS), including the use of managed services such as Amazon Bedrock.
  • Lead the evaluation, selection, and integration of leading large language models and AI services, including Anthropic, OpenAI, and Microsoft Copilot, ensuring appropriate use and performance.
  • Partner with business, product, and technology leaders to identify, prioritize, and design high-value AI use cases aligned to strategic objectives.
  • Establish and enforce AI governance frameworks, including policies, controls, and guardrails related to data usage, model risk, compliance, and responsible AI practices.
  • Define and oversee AI cybersecurity architecture, addressing risks such as data leakage, prompt injection, model misuse, and emerging AI threat vectors.
  • Provide architectural guidance for the development and integration of AI-enabled applications, ensuring solutions are production-ready, scalable, and maintainable.
  • Enable AI lifecycle management practices, including experimentation, deployment, monitoring, optimization, and retirement of AI solutions.
  • Collaborate with data, application development, security, legal, and compliance teams to ensure AI solutions align with enterprise standards, regulatory requirements, and risk management expectations.

Ymca Careers
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