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Remote Embedded Machine Learning Jobs in Minnesota

Eliassen Group

Senior AI/ML Engineer

Saint Paul, MN · Remote

$90 - $100/hr

Remote Our client seeks a Senior AI/ML Engineer to design and deliver cloud-native machine learning solutions on AWS. The role includes LLM orchestration, RAG pipelines, vector database integration ...

YO IT Consulting

UI/UX Designer - Remote

Finland, MN · Remote

Contractor Location: Remote Responsibilities Design, review, and critique intuitive user ... Preferred Qualifications Experience with AI, machine learning, or data-driven products. Background ...

THOMSON REUTERS

Lead Research Engineer

Eagan, MN · On-site +1

$104.50K - $137.70K/yr

... remote teams. * Be an Agile Person:With a strong sense of urgency and a desire to work in a fast ... Experienceintegrating Machine Learning solutionsinto production-grade softwarewith a sound ...

CSAA

... AI and machine learning, and owning the key performance indicators tied to their initiatives ... We embrace a remote-first culture through our Flexible Workplace. Most employees hold Home-Flex ...

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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 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 Minnesota? The most popular types of Embedded Machine Learning jobs in Minnesota are:
What are popular job titles related to Remote Embedded Machine Learning jobs in Minnesota? For Remote Embedded Machine Learning jobs in Minnesota, the most frequently searched job titles are:
What job categories do people searching Remote Embedded Machine Learning jobs in Minnesota look for? The top searched job categories for Remote Embedded Machine Learning jobs in Minnesota are:
What cities in Minnesota are hiring for Remote Embedded Machine Learning jobs? Cities in Minnesota with the most Remote Embedded Machine Learning job openings:
Remote Embedded Machine Learning jobs near you
Corporate Attorney - AI Reviewer - Remote

Corporate Attorney - AI Reviewer - Remote

micro1 AI

Minneapolis, MN • Remote

$100 - $150/hr

Part-time

Posted 7 days ago

Job description

Job Title: Attorney

Job Type: Contract

Location: Remote


Job Summary: In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input.


Key Responsibilities

  1. Design and implement robust legal rubrics for use in AI-driven document review and analysis processes.
  2. Conduct in-depth legal research and draft complex memoranda to guide AI model training and evaluation.
  3. Analyze large volumes of litigation documents to identify issues, trends, and data points vital for AI improvement.
  4. Collaborate with cross-functional teams to translate legal insights into actionable requirements for AI development.
  5. Oversee the quality and accuracy of AI outputs, providing feedback to enhance discovery management and motion practice capabilities.
  6. Develop case strategies and motion practice templates that inform machine learning models in legal contexts.
  7. Continuously review and refine rubric criteria to align with evolving legal standards and best practices.


Required Skills and Qualifications

  1. Juris Doctor (JD) degree and active bar membership.
  2. Active bar admission in at least one U.S. jurisdiction
  3. Minimum 5 years of litigation experience, with a strong track record managing document-intensive cases through discovery and dispositive motions.
  4. Exceptional legal research, writing, and analytical abilities, with particular skill in issue spotting and document analysis.
  5. Demonstrated expertise in case strategy development and motion practice.
  6. Proven ability to manage discovery processes and oversee complex legal document review projects.
  7. Outstanding written and verbal communication skills, with meticulous attention to detail.
  8. Technological acumen and comfort working in remote, digital-first environments.


Preferred Qualifications

  1. Law Review or Journal Editorial Experience, including substantive editing, cite-checking, and publication review of scholarly legal articles is highly prefered.

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