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Mobile Machine Learning Jobs in Ohio (NOW HIRING)

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Machinist

Cincinnati, OH · On-site

$20.75 - $28.50/hr

Machine products to tight tolerances * Reads and interprets specification manuals, blueprints ... Learning & Training opportunities Company Description Founded in 1911, we've always strived to ...

Hirekeyz Inc

API Testing Automation (SDET)

Columbus, OH · On-site

... machine learning, mobile, etc.) Preferred qualifications, capabilities, and skills Experience with Spinnaker is preferable Experience with Lambda using Python is preferable Exposure to data modeling ...

Hirekeyz Inc

API Testing Automation

Columbus, OH · On-site

$44.50 - $58.75/hr

... machine learning, mobile, etc.) Preferred qualifications, capabilities, and skills * Experience with Spinnaker is preferable * Experience with Lambda using Python is preferable * Exposure to data ...

Hirekeyz Inc

Web UI (Front End)

Columbus, OH · On-site

... machine learning, mobile, etc.) Preferred qualifications, capabilities, and skills Experience with Spinnaker is preferable Experience with Lambda using Python is preferable Exposure to data modeling ...

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Mobile Machine Learning information

See Ohio salary details

$11

$24

$113

How much do mobile machine learning jobs pay per hour?

As of May 31, 2026, the average hourly pay for mobile machine learning in Ohio is $24.08, according to ZipRecruiter salary data. Most workers in this role earn between $13.70 and $19.18 per hour, depending on experience, location, and employer.

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

To thrive as a Mobile Machine Learning Engineer, you need a solid background in computer science, machine learning, and mobile application development, often supported by a relevant degree and experience. Proficiency with ML frameworks (like TensorFlow Lite or Core ML), mobile platforms (Android/iOS), and deployment tools is typically required. Strong problem-solving skills, adaptability, and effective communication set standout professionals apart in this field. These skills are crucial for successfully developing, optimizing, and integrating machine learning models into efficient and user-friendly mobile applications.

What are some common challenges faced by Mobile Machine Learning engineers when deploying models on mobile devices?

Mobile Machine Learning engineers often encounter challenges related to limited computational resources and memory constraints on mobile devices. Optimizing models for efficient inference without significant loss in accuracy is a key hurdle, as is ensuring compatibility across different devices and operating systems. Additionally, balancing power consumption and real-time performance is critical, so engineers frequently collaborate with mobile app developers and hardware specialists to deliver seamless user experiences while maintaining model integrity.

What is mobile machine learning?

Mobile machine learning refers to the development and deployment of machine learning models on mobile devices such as smartphones and tablets. It enables apps to perform tasks like image recognition, language translation, and speech processing directly on the device without needing to send data to the cloud. This approach improves privacy, reduces latency, and can work even without an internet connection. Developers use frameworks like TensorFlow Lite, Core ML, and PyTorch Mobile to optimize models for the limited resources of mobile hardware.

What is the difference between Mobile Machine Learning vs Data Scientist?

AspectMobile Machine LearningData Scientist
Required CredentialsBachelor's in CS, ML, or related; experience with mobile platformsBachelor's or higher in CS, Statistics, or related; data analysis skills
Work EnvironmentMobile app development teams, on-device processingData analysis teams, research environments
Industry UsageMobile app companies, tech startupsFinance, healthcare, tech firms
Common Search/ComparisonYesYes

Mobile Machine Learning focuses on developing ML models optimized for mobile devices and integrating them into mobile apps. Data Scientists analyze large datasets to extract insights and build predictive models across various industries. While both roles require programming and ML knowledge, Mobile Machine Learning emphasizes on-device deployment and mobile platform expertise, whereas Data Scientists focus on data analysis and model development for broader applications.

What are the most commonly searched types of Machine Learning jobs in Ohio? The most popular types of Machine Learning jobs in Ohio are:
What are popular job titles related to Mobile Machine Learning jobs in Ohio? For Mobile Machine Learning jobs in Ohio, the most frequently searched job titles are:
What job categories do people searching Mobile Machine Learning jobs in Ohio look for? The top searched job categories for Mobile Machine Learning jobs in Ohio are:
What cities in Ohio are hiring for Mobile Machine Learning jobs? Cities in Ohio with the most Mobile Machine Learning job openings:
Mobile Machine Learning jobs near you

Argo Developer

Purple Drive Technologies

Columbus, OH • On-site

Full-time

Posted 21 days ago

Job description

Overview:
Skills required :
• C# development
• Java development
• PowerShell scripting
• Alogent Branch deposit engine knowledge
Roles & Responsibilities
• Hands on Argo Data Resource Corp. experience
• Seasoned developer with Teller Retail banking experience with Argo ADS software.
• Formal training or certification on software engineering concepts and (3)+ years applied experience
• 3+ years of hands-on experience developing in Argo Traditional Client for Teller platform
• Current knowledge of Argo version 6 development objects, including Enablers, WOGS, AOGS, host communications, Electronic Journal (EJ), and Totals
• Hands-on practical experience in system design, application development, testing, and operational stability
• Proficient in coding in one or more languages
• Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages
• Overall knowledge of the Software Development Life Cycle
• Solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
• Demonstrated knowledge of software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.

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