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Remote Embedded Machine Learning Jobs in Urbana, OH

AVP Applied AI

Columbus, OH ยท On-site +1

The Assistant Vice President (AVP), Applied AI leads data science, traditional machine learning ... This role can have a Hybrid or Remote work schedule. Candidates who live near one of our office ...

AI/ML Engineer, Senior

Dayton, OH ยท On-site +1

$99K - $225K/yr

Remote Work: No Job Number: R0242678 Location: Dayton,OH,US Share job via: Share AI/ML Engineer, Senior The Opportunity: As a Senior Artificial Intelligence and Machine Learning (AI/ML) Engineer, you ...

Lead AI Engineer - AWS Platform

Columbus, OH ยท On-site +1

$130K - $190K/yr

Build machine learning models that automate their training, validation, monitoring, and retraining ... Flexible work schedules and hybrid/remote options for eligible positions * Educational assistance ...

AI/ML Engineer, Mid

Dayton, OH ยท On-site +1

$77K - $176K/yr

Remote Work: No Job Number: R0242676 Location: Dayton,OH,US Share job via: Share AI/ML Engineer, Mid The Opportunity: As an Artificial Intelligence and Machine Learning (AI/ML) Engineer, you will ...

AI/ML Engineer, Mid

Dayton, OH ยท On-site +1

$77K - $176K/yr

Remote Work: No Job Number: R0243806 Location: Dayton,OH,US Share job via: Share AI/ML Engineer, Mid The Opportunity: As an Artificial Intelligence and Machine Learning (AI/ML) Engineer, you will ...

AI and ML Engineer, Mid

Dayton, OH ยท On-site +1

$77K - $176K/yr

Remote Work: No Job Number: R0245636 Location: Dayton,OH,US Share job via: Share AI and ML Engineer, Mid The Opportunity: As an Artificial Intelligence and Machine Learning (AI/ML) Engineer, you will ...

Showing results 21-40

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.

Distinguished AI/ML Engineer

Frontier Technology Inc.

Dayton, OH โ€ข Remote

Full-time

Re-posted 15 days ago


Job description

Overview

FTI Defense delivers mission-focused solutions to the Department of Defense and Intelligence Community through advanced engineering, digital transformation, and program execution expertise. We help our customers solve complex challenges and achieve mission success by integrating people, process, and technology.

FTI Defense is seeking a Distinguished AI/ML Engineer to serve as a technical leader, architect, and integrator - designing, building, deploying, and sustaining AI systems that transform complex mission data into trusted, explainable insights.

This is a hands-on builder role, not an analytics management position. The ideal candidate is equally comfortable writing model code, standing up ML pipelines, and integrating AI inference services into operational systems within secure environments. The right candidate blends deep AI/ML engineering expertise with system-level architecture leadership and an ability to unify data engineering, simulation modeling, and responsible AI principles into scalable, mission-ready capabilities.

Responsibilities
  • Architect and integrate hybrid AI systems that combine traditional machine learning, deep learning, large language models (LLMs), and retrieval-augmented generation (RAG) pipelines.
  • Design and deploy scalable AI architectures including APIs, microservices, and model-serving frameworks that integrate seamlessly with analytic, simulation, or operational systems.
  • Lead the full AI/ML lifecycle - from data ingestion and feature engineering through training, deployment, and sustainment within secure DoD environments (IL5/IL6, ATO, GovCloud).
  • Engineer event-driven data pipelines and feature stores for both structured and unstructured data, including text, imagery, and simulation outputs.
  • Ensure Responsible AI practices by embedding traceability, explainability, and confidence scoring into deployed systems.
  • Implement and maintain MLOps pipelines (MLflow, Kubeflow, Airflow, Docker/Kubernetes) to support continuous integration, retraining, and drift detection.
  • Transition R&D prototypes into production, optimizing for mission constraints such as limited compute, edge environments, or disconnected operations.
  • Provide technical leadership and mentorship, setting standards for model quality, architectural design, and ethical AI deployment across programs.
  • Collaborate across engineering, data, and modeling teams to unify FTI's AI portfolio, ensuring interoperability and reuse across mission systems.
  • Support proposal and solution development, providing technical inputs for AI/ML architectures, data strategies, and Responsible AI assurance frameworks.
Education/Qualifications
  • Active Secret clearance required; TS/SCI strongly preferred.
  • Bachelor's degree in Computer Science, Engineering, or a related technical field (Master's or Ph.D. preferred).
  • 10+ years of overall experience in AI/ML development, with 5+ years designing and deploying scalable AI/ML architectures, including at least two full lifecycle implementations (from prototype to operational system).
  • Proficiency in Python, PyTorch, TensorFlow, and modern ML frameworks.
  • Experience designing or deploying systems using vector databases (Milvus, Pinecone, Weaviate), knowledge graphs, and semantic search frameworks.
  • Proven ability to design event-driven data pipelines using Databricks, Spark, Flink, or Kafka.
  • Demonstrated experience deploying AI/ML systems in secure, classified, or edge environments.
  • Familiarity with Responsible AI and assurance principles, including bias detection, explainability, human-machine teaming, and hallucination prevention.
  • Experience integrating AI models into simulation, modeling, or operational planning systems is highly desirable.
  • Experience transitioning R&D systems into accredited production environments.
  • Strong communication and mentoring skills, with the ability to lead technically while remaining deeply hands-on.

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Employment Type: FULL_TIME