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Remote Embedded Machine Learning Jobs in Minnesota

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 ...

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 ...

... platforms, machine learning workloads, cloud infrastructure, and data integrations. * Lead root ... We embrace a remote-first culture through our Flexible Workplace. Most employees hold Home-Flex ...

Senior Data Engineer

Rochester, MN · On-site +1

$103K - $140K/yr

... remote, team-oriented environment are also necessary. Develops and deploys data pipelines, integrations and transformations to support analytics and machine learning applications and solutions as ...

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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 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 Minnesota? The most popular types of 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:
Director Data Science - Remote or Hybrid in MN or DC

Director Data Science - Remote or Hybrid in MN or DC

UnitedHealth Group

Eden Prairie, MN • On-site, Remote

Full-time

Retirement

Posted 3 days ago

New


UnitedHealth Group rating

7.6

Company rating: 7.6 out of 10

Based on 145 frontline employees who took The Breakroom Quiz

191st of 884 rated healthcare providers


Job description

Optum Tech is a global leader in health care innovation. Our teams develop cutting-edge solutions that help people live healthier lives and help make the health system work better for everyone. From advanced data analytics and AI to cybersecurity, we use innovative approaches to solve some of health care's most complex challenges. Your contributions here have the potential to change lives. Ready to build the next breakthrough? Join us to start Caring. Connecting. Growing together.


You'll enjoy the flexibility to work remotely * from anywhere within the U.S. as you take on some tough challenges.    For all hires in the Minneapolis or Washington, D.C. area, you will be required to work in the office a minimum of four days per week.
Primary Responsibilities:

  • Define enterprise data science strategy: Own and drive the technical strategy for applied machine learning, Generative AI, Agentic AI, and advanced analytics across multiple domains and healthcare use cases
  • Lead development of advanced ML, GenAI, and agentic solutions: Provide hands-on technical direction for the design, development, and deployment of machine learning, deep learning, time-series, survival analysis, large language model (LLM), and agent-based AI systems in production environments
  • Establish modeling standards and best practices: Define and standardize modeling frameworks, feature engineering approaches, prompt and context engineering practices, evaluation methodologies, and validation standards across data science teams
  • Architect scalable ML and GenAI systems: Guide the design of production-grade ML and LLM systems including data pipelines, feature stores, retrieval-augmented generation (RAG), model serving infrastructure, agent orchestration frameworks, monitoring, and retraining workflows
  • Ensure responsible and reliable AI deployment: Implement consistent practices for model interpretability, explainability, bias assessment, fairness evaluation, guardrails, human oversight, and lifecycle management across deployed predictive, generative, and agentic AI systems
  • Oversee experimentation and performance monitoring: Define experimentation, benchmarking, and monitoring strategies including drift detection, recalibration, LLM evaluation, hallucination and safety checks, tool-use reliability, and performance management
  • Provide technical leadership and mentorship: Mentor principal and senior data scientists, review technical designs and modeling decisions, and provide guidance for complex analytical, GenAI, and agentic AI challenges
  • Influence cross-functional AI delivery: Partner with engineering, data, security, product, and platform teams to align data science solutions with enterprise platforms, infrastructure, reliability requirements, AI governance expectations, and executive priorities
  • Partner with payment integrity, clinical, claims, compliance, legal, product, and operations stakeholders to translate business problems such as overpayment detection, coding validation, policy adherence, aberrant billing patterns, and prepay/postpay review into scalable AI and analytics solutions
     

You'll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in. 
 

Required Qualifications:

  • 12 years of experience in data science, machine learning, or advanced analytics with 8 years developing and deploying production ML models
  • 8 years of experience using Python-based data science ecosystems (for example Pandas, NumPy, scikit-learn, PyTorch, or equivalent) and advanced SQL for large-scale analytics, experimentation, and data transformation
  • 7 years of experience in senior data science or technical leadership roles influencing modeling approaches, reviewing analytical work across teams, setting standards for model development and validation, and translating complex technical tradeoffs for senior stakeholders
  • 6 years of experience designing, deploying, or supporting production ML systems, including model serving, monitoring, retraining workflows, experimentation frameworks, ML lifecycle management, and evaluation of LLM or GenAI applications
  • 6 years of experience working with healthcare data such as claims, EHR, pharmacy, or laboratory datasets, including familiarity with healthcare coding systems such as ICD, CPT, NDC, SNOMED, and LOINC, as well as data interoperability standards including FHIR or HL7
  • 3 years of experience designing, building, or operationalizing Generative AI or LLM-based systems
  • 3 years of experience applying data science, machine learning, advanced analytics, or AI techniques to healthcare program integrity, payment integrity, fraud, waste, and abuse, claims payment accuracy, improper payment reduction, coding validation, provider behavior analytics, or related healthcare financial integrity use cases
  • 1 years of experience with Agentic AI concepts and implementations such as AI agents, agentic skills, model context protocols (MCPs), agent-to-agent (A2A) patterns, tool use, orchestration frameworks, or autonomous workflow execution
    *All employees working remotely will be required to adhere to UnitedHealth Group's Telecommuter Policy.


Pay is based on several factors including but not limited to local labor markets, education, work experience, certifications, etc. In addition to your salary, we offer benefits such as, a comprehensive benefits package, incentive and recognition programs, equity stock purchase and 401k contribution (all benefits are subject to eligibility requirements). No matter where or when you begin a career with us, you'll find a far-reaching choice of benefits and incentives. The salary for this role will range from $134,600 - $230,800 annually based on full-time employment. We comply with all minimum wage laws as applicable.

Application Deadline: This will be posted for a minimum of 2 business days or until a sufficient candidate pool has been collected. Job posting may come down early due to volume of applicants.
 

At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone-of every race, gender, sexuality, age, location and income-deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission.


UnitedHealth Group is an Equal Employment Opportunity employer under applicable law and qualified applicants will receive consideration for employment without regard to race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, or protected veteran status, or any other characteristic protected by local, state, or federal laws, rules, or regulations.


UnitedHealth Group is a drug - free workplace. Candidates are required to pass a drug test before beginning employment. 


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