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Remote Embedded Machine Learning Jobs in Urbana, OH

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Remote Embedded Machine Learning information

See Urbana, OH salary details

$65.7K

$144K

$163.3K

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

As of Aug 7, 2026, the average yearly pay for remote embedded machine learning in Urbana, OH is $143,960.00, according to ZipRecruiter salary data. Most workers in this role earn between $123,400.00 and $162,400.00 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?

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.

Sourcing - Director, Data Science: Data Science Tools

Liberty Information Technology Limited

Columbus, OH • On-site, Remote

Full-time

Re-posted 24 days ago


Job description

Description

The Data Science Infrastructure organization within USRM is hiring a Senior Technical Professional, Data Scientist to join the Data Science Tools team. This role will focus on improving the end-to-end modeling workflow for USRM Data Science by building internal tools, pipelines, and applications that streamline model development, evaluation, deployment, and iteration. The ideal candidate is highly technical, proactive, and motivated by building systems that help other data scientists work more efficiently.

**Candidates who live within 50 miles of Boston, MA; Portsmouth, NH; Seattle, WA; Columbus, OH; or Plano, TX will follow a hybrid schedule, coming into the office two days per week. Otherwise, this role is remote with occasional travel. **

Responsibilities:

  • Design and build internal tools, pipelines, and applications that improve model development, evaluation, and deployment
  • Own strategy and roadmaps for improving data science workflows and tooling across USRM
  • Design, build, and maintain Python packages used across the organization
  • Evaluate and implement AI agent capabilities in tooling using approaches such as MCP, RAG, PydanticAI, LangChain, or related frameworks
  • Work with workflow and modeling tools such as Luigi, Airflow, Celery, MLflow, H2O, scikit-learn, Optuna, and LightGBM, as well as Python development tools such as Pydantic, FastAPI, uv, ruff, and pytest
  • Promote MLOps and AI agent best practices in collaboration with groups such as Enterprise Data & Data Science
  • Stay current on developments in open-source data science frameworks, MLOps, and agentic coding practices
  • Help shape the direction of the Tools team and contribute to a culture of ownership, collaboration, and continuous improvement

The ideal candidate will have:

  • Professional experience building and maintaining Python-based data science or Machine Learning tooling used by multiple end users or teams
  • Worked with any of the following in a professional setting: Git, Bash/shell scripting, uv, pre-commit, ruff, pytest, or Pydantic
  • Built, deployed, or maintained workflows or pipelines using any of the following: Airflow, Luigi, Celery, Databricks, or MLflow
  • Implemented or supported AI/LLM-based tooling using frameworks such as PydanticAI, LangChain, MCP, or RAG
  • Developed, reviewed, or maintained internal Python packages, APIs, or data science applications using tools such as FastAPI, Streamlit, Dash, NiceGUI, or Plotly
  • Applied agentic AI techniques in day-to-day development and incorporate AI capabilities directly into tools and applications where they create meaningful value for data scientists
Qualifications
  • Broad knowledge of predictive analytic techniques and statistical diagnostics of models.
  • Advanced knowledge of predictive toolset; reflects as expert resource for tool development.
  • Demonstrated ability to exchange ideas and convey complex information clearly and concisely.
  • Ability to establish and build relationships within and outside the organization.
  • Ability to give effective training and presentations to management and other groups.
  • Ability to use results of analysis to persuade team, department management or senior management to a particular course of action.
  • Broad knowledge of business drivers and market context.
  • Has a value driven perspective with regard to understanding of work context and impact.
  • Competencies typically acquired through a Ph.D. degree (in Statistics, Mathematics, Economics, Actuarial Science or other scientific field of study) and a minimum of 3 years of relevant experience, a Master`s degree (scientific field of study) and a minimum of  6 years of relevant experience or may be acquired through a Bachelor`s degree (scientific field of study) and a minimum of  8 years of relevant experience.

Employees may apply for a new role after completing 12 months of employment in their current position.

Employees should review all role requirements and apply only for positions for which they are eligible. Hiring processes may vary by country, including differences in procedures, requirements, and timelines.  For country-specific details, please consult your local recruiting / HR team.

About Us

Pay Philosophy: The typical starting salary range for this role is determined by a number of factors including skills, experience, education, certifications and location. The full salary range for this role reflects the competitive labor market value for all employees in these positions across the national market and provides an opportunity to progress as employees grow and develop within the role. Some roles at Liberty Mutual have a corresponding compensation plan which may include commission and/or bonus earnings at rates that vary based on multiple factors set forth in the compensation plan for the role.At Liberty Mutual, our goal is to create a workplace where everyone feels valued, supported, and can thrive. We build an environment that welcomes a wide range of perspectives and experiences, with inclusion embedded in every aspect of our culture and reflected in everyday interactions. This comes to life through comprehensive benefits, workplace flexibility, professional development opportunities, and a host of opportunities provided through our Employee Resource Groups. Each employee plays a role in creating our inclusive culture, which supports every individual to do their best work. Together, we cultivate a community where everyone can make a meaningful impact for our business, our customers, and the communities we serve.We value your hard work, integrity and commitment to make things better, and we put people first by offering you benefits that support your life and well-being. To learn more about our benefit offerings please visit: https://LMI.co/BenefitsLiberty Mutual is an equal opportunity employer. We will not tolerate discrimination on the basis of race, color, national origin, sex, sexual orientation, gender identity, religion, age, disability, veteran's status, pregnancy, genetic information or on any basis prohibited by federal, state or local law.Fair Chance Notices

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Employment Type: FULL_TIME