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Machine Learning Object Detection Jobs in Ohio (NOW HIRING)

MMB Solutions LLC

Systems Engineer

Dayton, OH · On-site

$135K - $180K/yr

This position will support development of a data architecture and machine learning experimentation ... Rogue handset detection scenarios * Integrated sensing and communications (ISAC) experiments ...

GRVTY

Software Engineer, Senior

Dayton, OH · On-site +1

$119K - $157K/yr

Develop and integrate machine learning workflows - including training data preparation, model ... Expert-level Python programming, including object-oriented design, modular architecture, and ...

GRVTY

Software Engineer, Senior

Dayton, OH · On-site +1

$119K - $157K/yr

Develop and integrate machine learning workflows - including training data preparation, model ... Expert-level Python programming, including object-oriented design, modular architecture, and ...

GRVTY

Software Engineer, Senior

Dayton, OH · On-site

$119K - $157K/yr

Develop and integrate machine learning workflows - including training data preparation, model ... Expert-level Python programming, including object-oriented design, modular architecture, and ...

GRVTY

Software Engineer, Senior

Dayton, OH · On-site +1

$119K - $157K/yr

Develop and integrate machine learning workflows -- including training data preparation, model ... Expert-level Python programming, including object-oriented design, modular architecture, and ...

AssetWatch, Inc.

Head of AI

Columbus, OH

AssetWatch has a unique opportunity to scale how LLMs, Agents, machine learning, and data science ... Experience with time-series data, signal processing, anomaly detection, or sensor-driven products.

New

Ferroglobe

Business Data Analyst

Marietta, OH · On-site

Apply statistical and machine learning techniques to build predictive models and conduct exploratory analysis to identify patterns, detect anomalies, and uncover insights that support business ...

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Machine Learning Object Detection information

What are the key skills and qualifications needed to thrive as a Machine Learning Object Detection Engineer, and why are they important?

To excel as a Machine Learning Object Detection Engineer, you need a solid background in computer science, mathematics, and deep learning principles, often backed by a relevant degree and experience in computer vision. Familiarity with frameworks like TensorFlow, PyTorch, and OpenCV, as well as experience with annotation tools and GPU computing, is typically required. Strong problem-solving abilities, attention to detail, and effective communication are vital soft skills for collaborating with cross-functional teams and addressing complex challenges. These competencies ensure accurate model development, efficient deployment, and continual improvement of object detection systems in real-world applications.

What are some common challenges faced when working on machine learning object detection projects?

One of the main challenges in machine learning object detection roles is dealing with the quality and quantity of annotated data, as accurate labeling is essential for model performance. Another common challenge is managing variations in object scale, lighting, and occlusion within real-world images, which can affect detection accuracy. Additionally, balancing model accuracy with computational efficiency—especially for real-time applications—often requires careful model selection and optimization. Collaboration with data engineers and domain experts is also typical to ensure data relevance and model applicability.

What is machine learning object detection?

Machine learning object detection is a field within artificial intelligence that focuses on identifying and locating objects within images or videos. It uses algorithms and deep learning models, such as convolutional neural networks (CNNs), to analyze visual data and predict the presence and position of various objects. Object detection is widely used in applications like autonomous vehicles, security surveillance, and image search. The process typically involves training models on labeled datasets so they can accurately detect and classify multiple objects in complex scenes.
What are popular job titles related to Machine Learning Object Detection jobs in Ohio? For Machine Learning Object Detection jobs in Ohio, the most frequently searched job titles are:
What job categories do people searching Machine Learning Object Detection jobs in Ohio look for? The top searched job categories for Machine Learning Object Detection jobs in Ohio are:
What cities in Ohio are hiring for Machine Learning Object Detection jobs? Cities in Ohio with the most Machine Learning Object Detection job openings:
Machine Learning Object Detection jobs near you

Systems Engineer

MMB Solutions LLC

Dayton, OH • On-site

$135K - $180K/yr

Full-time

Re-posted 13 days ago

Job description

MMB Solutions is seeking a mid-level Systems Engineer to support the design, development, and operation of advanced wireless experimentation infrastructure for the Open 6G Testbed program. The engineer will work on next-generation wireless research involving 5G/6G telemetry architectures, AI-driven network experimentation, spectrum analytics, and integrated sensing and communications (ISAC).
This position will support development of a data architecture and machine learning experimentation platform used to collect, enrich, analyze, and operationalize wireless telemetry data from advanced radio systems operating in research environments.
The Systems Engineer will collaborate with software engineers, RF engineers, and data scientists to build scalable telemetry pipelines, research data infrastructure, and AI-enabled wireless experimentation platforms.
This role is based in Dayton, Ohio with the possibility of remote work.
Requirements
U.S. Citizenship
Bachelors' Degree in one of the following:
  • Electrical Engeering
  • Computer Science
  • Systems Engineering
  • Computer Engineering
  • Wireless Communications

Key Responsibilities
Wireless Telemetry Architecture
Design and implement systems that collect and process telemetry data from advanced wireless networks and sensing infrastructure.
Responsibilities include:
  • Designing telemetry collection architectures for 5G and emerging 6G network environments
  • Integrating telemetry from multiple wireless interfaces including:
    • O-RAN E2 interfaces
    • F1 interfaces
    • Core network signaling (N1/N2/N3)
    • RRC signaling and RAN performance metrics
  • Supporting ingestion of telemetry sources such as:
    • User equipment activity
    • Spectrum observations
    • Network performance counters
  • Developing telemetry schemas and system documentation for research datasets.

Wireless Research Data Infrastructure
Support the development of a centralized wireless research data lake used to store and analyze large volumes of telemetry and experimentation data.
Responsibilities include:
  • Designing and supporting scalable data ingestion pipelines
  • Supporting schema enforcement and structured dataset management
  • Developing indexing and query capabilities for telemetry datasets
  • Integrating telemetry sources into research analytics environments
  • Supporting data lifecycle management for wireless experimentation datasets.

Data Enrichment and Machine Learning Integration
Develop and support automated data enrichment pipelines to convert raw telemetry into AI-ready wireless datasets.
Responsibilities include:
  • Implementing automated tagging and labeling pipelines
  • Integrating enrichment sources including:
    • Spectrum monitoring systems
    • Environmental sensors
    • Geospatial positioning systems
    • Experiment scenario metadata
  • Supporting machine learning feature extraction and dataset generation.

Wireless Experimentation Support
Assist in the design and execution of wireless experimentation events within the Open 6G Testbed.
Example experiment areas include:
  • Wireless interference injection scenarios
  • Spectrum sharing experiments
  • Rogue handset detection scenarios
  • Integrated sensing and communications (ISAC) experiments

Responsibilities include:
  • Experiment planning and system integration
  • Dataset generation and collection
  • Experiment documentation and reporting.

AI Model Deployment and Orchestration
Support deployment of an AI model lifecycle management framework integrated with wireless telemetry pipelines.
Responsibilities include:
  • Supporting model versioning and lifecycle management
  • Integrating machine learning models with wireless telemetry systems
  • Deploying distributed inference capabilities across network infrastructure

Supporting AI experimentation using structured wireless datasets.

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