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Embedded Machine Learning Jobs in Columbus, OH (NOW HIRING)

Pwc

US Tech - AI Engineering Manager

Columbus, OH · On-site

$73.50K - $244K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced ... and embedded automated quality gates across CI/CD pipelines Travel Requirements Up to 20% Job ...

Rosennxt

Workgroup Lead

Columbus, OH · On-site +1

$99K - $130.40K/yr

This involves programming near hardware-software interfaces, developing or modifying embedded ... machine learning models to deliver high quality solutions to our partners. Rosenxt USA seeks to add ...

Vertiv Co

Advanced Thermal Controls Engineer

Westerville, OH · On-site

$80.50K - $104.10K/yr

Demonstrated expertise in system validation for controls platforms (embedded, automation, software ... Machine learning and artificial intelligence technologies Travel: Willingness to travel up to 10% ...

Reveleer

Applied Analytics Intern

New Albany, OH · On-site

$25/hr

Summer 1: Operations + Applied Experimentation Interns will be embedded within the Medical Record ... Exposure to statistics, experimentation, or basic machine learning concepts * Ability to translate ...

Reveleer

Applied Analytics Intern

New Albany, OH

$25/hr

Summer 1: Operations + Applied Experimentation Interns will be embedded within the Medical Record ... Exposure to statistics, experimentation, or basic machine learning concepts * Ability to translate ...

ACCEL Schools

Custodian - SY 26-27

Columbus, OH

$16 - $20/hr

CHATA's global learning community represents 40 ethnicities and celebrates cultures through World ... Properly maintain all school tools, supplies, and machinery that are used in day to day operations

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Embedded Machine Learning information

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$67.6K

$148.2K

$168.1K

How much do embedded machine learning jobs pay per year?

As of May 29, 2026, the average yearly pay for embedded machine learning in Columbus, OH is $148,153.00, according to ZipRecruiter salary data. Most workers in this role earn between $127,000.00 and $167,100.00 per year, depending on experience, location, and employer.

What is an Embedded Machine Learning job?

An Embedded Machine Learning job involves developing and optimizing machine learning models to run efficiently on resource-constrained devices like microcontrollers, edge devices, and IoT hardware. Professionals in this role work on model compression, low-power inference, and real-time processing, ensuring AI capabilities can function without relying on cloud computing. Responsibilities often include data preprocessing, feature extraction, model training, and deployment on embedded systems using frameworks like TensorFlow Lite or Edge Impulse.

What are the key skills and qualifications needed to thrive in the Embedded Machine Learning position, and why are they important?

To thrive in Embedded Machine Learning, you should have expertise in machine learning algorithms, embedded systems programming (e.g., C/C++, Python), and a solid understanding of hardware-software integration, typically backed by a degree in computer engineering, electrical engineering, or a related field. Familiarity with edge AI tools (such as TensorFlow Lite, ONNX, or Edge Impulse), microcontrollers, and real-time operating systems is highly valued, alongside relevant certifications such as Embedded Systems or AI certificates. Strong problem-solving skills, effective communication, and the ability to work cross-functionally are crucial soft skills in this field. These qualifications and qualities are vital for creating efficient, reliable AI solutions that operate seamlessly within resource-constrained environments and interdisciplinary project teams.

What are some common challenges faced by professionals working in embedded machine learning roles?

Professionals in embedded machine learning roles often face the challenge of optimizing machine learning models to run efficiently on resource-constrained hardware, such as microcontrollers or edge devices with limited memory and processing power. Balancing model accuracy, inference speed, and energy consumption can require creative problem-solving and deep knowledge of both hardware and software. Additionally, collaboration with hardware engineers, data scientists, and software developers is key, as projects typically require cross-functional teamwork to meet performance and deployment goals. Staying current with rapidly evolving tools and best practices is also important in this dynamic field.
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Embedded Machine Learning jobs near you
Lead Data Scientist - US Remote

Lead Data Scientist - US Remote

Hexion, Inc.

Columbus, OH • On-site, Remote

Full-time

Posted 4 days ago

Job description

Company Overview
Imagine Everything. Build the Future with Hexion.
At Hexion, we push boundaries, rethink possibilities, and create real impact. We activate science to deliver progress-developing breakthrough solutions that strengthen industries, protect communities, and drive a more sustainable future.
This is where bold thinkers, problem-solvers, and innovators come together to shape what's next. Whether you're engineering advanced materials, transforming manufacturing technologies, or leading strategic innovation, your ideas and actions leave a lasting mark. We cultivate an inclusive culture of growth, collaboration, and accountability, ensuring every contribution propels us forward.
We don't follow the status quo-we challenge it, disrupt it, and improve it. Every role at Hexion is part of something bigger.
We invest in innovation, sustainability, and continuous development-equipping you with the tools, training, and opportunities to excel. With an unwavering commitment to safety, partnership, belonging, and impact, we empower you to lead change and strengthen industries worldwide.
Your Future Starts Here.
If you're ready to push limits, reimagine what's possible, and create the extraordinary, Hexion is where you belong.
Anything is possible when you imagine everything.
Job Responsibilities
  • Lead complex data science and machine learning initiatives supporting supply chain, manufacturing operations, capacity planning, demand forecasting, and operational decision-making.
  • Design, develop, and own advanced ML solutions - including predictive models, time-series forecasting, optimization, and decision-support systems - scoped to supply chain and manufacturing use cases.
  • Build, train, evaluate, and interpret machine learning models (regression, classification, clustering, forecasting) to quantify supply chain drivers, surface optimization opportunities, and improve operational outcomes.
  • Develop and operationalize analytics and ML solutions using Databricks (Python / SQL / PySpark) for large-scale data processing, model development, and experimentation.
  • Design and build multi-agent AI systems - including orchestrator-executor architectures, tool-calling agents, and RAG-based decision support - using frameworks such as Azure AI Foundry, AutoGen, Semantic Kernel, or LangChain/LangGraph.
  • Implement and extend solutions using the MCP to enable AI agents to access and act on enterprise data systems in supply chain and manufacturing contexts.
  • Apply data science best practices including feature engineering, model validation, performance monitoring, reproducibility, and documentation.
  • Partner with Supply Chain & Procurement leadership, Manufacturing Ops, Process Engineering, Demand Planning, and IT to translate ambiguous business problems into structured ML and AI approaches.
  • Develop and maintain self-service, automated, and AI-enabled analytics workflows that reduce manual effort and improve decision latency.
  • Leverage Azure AI Foundry, Microsoft Copilot Studio, and Microsoft 365 Copilot extensibility to prototype and deploy AI-powered analytics and agent-based decision-support tools.
  • Produce executive-ready insights through clear storytelling, visualizations, and recommendations using Power BI or embedded analytics.
  • Set technical direction, establish reusable ML and AI frameworks, and mentor junior and mid-level data scientists across the team.
  • Ensure high standards of data quality, governance, model validation, and explainability.

Minimum Qualifications
Education & Experience (one of the following):
  • Master's degree in Statistics, Mathematics, Industrial Engineering, Data Science, Computer Science, Engineering, or a related quantitative field with 5+ years of relevant data science/analytics experience, OR
  • Bachelor's degree in the same or related fields with 8+ years of relevant data science / analytics experience.

Technical:
  • Demonstrated track record delivering advanced ML and data science solutions in supply chain, manufacturing, or industrial domains.
  • Strong hands-on experience with machine learning and statistical modeling - development, interpretation, and operational business application.
  • Strong proficiency in Databricks (Python, SQL, PySpark, Delta Lake).
  • Hands-on experience with the MCP - building or consuming MCP servers/clients to connect AI agents to enterprise data systems, APIs, or ERP modules.
  • Hands-on experience with multi-agent system design - architecting multi-agent systems using AutoGen, Semantic Kernel, LangChain/LangGraph, or Azure AI Agent Service; orchestrator-executor patterns, tool calling, memory management, and agent coordination.
  • Compulsory - must have hands-on experience with one or more of the following:
    • Azure AI Foundry
    • Microsoft Copilot Studio
    • Microsoft 365 Copilot extensibility
    • Microsoft Power Platform (Power Automate, Power BI)
  • Ability to translate complex business problems into ML / AI solutions and communicate findings to both technical and executive audiences.
  • Strong stakeholder management and cross-functional collaboration skills.

Preferred Qualifications
  • Experience operationalizing ML models into production in supply chain or manufacturing environments.
  • Familiarity with SAP ECC / S/4HANA supply chain and manufacturing modules (MM, PP, PM, SD).
  • Strong Power BI experience - semantic modeling, performance optimization, executive dashboard design.
  • Exposure to MLOps on Azure (Azure ML, MLflow, Databricks Asset Bundles, CI/CD for analytics artifacts).
  • Experience designing operational KPI frameworks (MAPE, OTIF, service level, OEE, downtime).
  • Experience with statistical / simulation methods (Monte Carlo, scenario analysis, sensitivity analysis) applied to operations and supply chain.
  • Familiarity with Palantir Foundry (pipelines, ontology, Workshop, AIP).
  • Proven experience mentoring data scientists or leading end-to-end analytics initiatives.
  • Familiarity with cloud-native data architectures and governed data platforms.

Other
We are an Equal Opportunity, Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to gender, pregnancy, race, national origin, religion, age, sexual orientation, gender identity, veteran or military status, status as a qualified individual with a disability or any other characteristic protected by law.
To be considered for this position candidates are required to submit an application for employment through our career site and, be at least 18 years of age. Any offer of employment will be conditioned upon successful completion of a drug test and background investigation, as well as authorization for the Company to conduct additional periodic background checks as required by the Chemical Facility Anti-Terrorism Standards (CFATS) or regulations adopted by the department of Homeland Security or other regulatory agencies. A prior criminal record is not an automatic bar to employment, and the Company will conduct an individualized assessment and reassessment, consistent with applicable law, prior to making any final employment decision.

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