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Mobile Machine Learning Jobs in Utah (NOW HIRING)

Basic knowledge of core machine learning concepts * Experience with real-time audio processing and ... MiTek's experience in designing, manufacturing, and selling high performance commercial, mobile ...

... mobile or web applications. Academic or professional experience must include: experience with ... statistical machine learning, and advanced programming algorithms; experience with computer ...

... mobile or web applications. Academic or professional experience must include: experience with ... statistical machine learning, and advanced programming algorithms; experience with computer ...

... mobile or web applications. Academic or professional experience must include: experience with ... statistical machine learning, and advanced programming algorithms; experience with computer ...

... mobile and wireless technologies • Proficiency of various operating systems: Windows, iOS ... machine learning, and mission support and engineering services to the Intelligence Community ...

... mobile and wireless technologies • Proficiency of various operating systems: Windows, iOS ... machine learning, and mission support and engineering services to the Intelligence Community ...

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Mobile Machine Learning information

See Utah salary details

$11

$23

$108

How much do mobile machine learning jobs pay per hour?

As of May 30, 2026, the average hourly pay for mobile machine learning in Utah is $23.05, according to ZipRecruiter salary data. Most workers in this role earn between $13.12 and $18.37 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 Utah? The most popular types of Machine Learning jobs in Utah are:
What are popular job titles related to Mobile Machine Learning jobs in Utah? For Mobile Machine Learning jobs in Utah, the most frequently searched job titles are:
What cities in Utah are hiring for Mobile Machine Learning jobs? Cities in Utah with the most Mobile Machine Learning job openings:

Cyber Security Analyst (Level 1) with Security Clearance

Anonymous Employer

Hill Air Force Base, UT

Other

Posted 10 days ago


Job description

Cyber Security Analyst (Level 1)
About the Role: 
As a Cyber Security Analyst, you'll be on the front lines, defending Department of Defense networks from evolving cyber threats. You will be a key member of our 24x7 security operations team, responsible for:  • Analyzing real-time cyber threat intelligence to stay ahead of emerging threats.  • Correlating security events to identify and prioritize potential incidents.  • Conducting in-depth network traffic analysis using raw packet data to uncover malicious activity.  • Collaborating with incident response teams to contain and eradicate threats. 
Shift Opportunities: 
We offer flexible shift options to accommodate your needs. The primary available shifts are: 7:00 AM - 3:00 PM, 3:00 PM - 11:00 PM, & 11:00 PM - 7:00 AM. Shift assignments will be based on program requirements and your preference, but some flexibility may be required. 
Locations: 
Team members can be based out of one of the following locations depending on position availability: Hill AFB, UT, Scott AFB, IL, and Columbus, OH.  
Primary Responsibilities:  • Investigate alerts generated from endpoints, IDS/IPS, NetFlow data, and custom sensors to detect compromises on customer networks.  • Analyze extensive log files, pivot between diverse datasets, and correlate evidence to support incident investigations, creating detailed technical reports outlining your findings.  • Triage security alerts to rapidly identify malicious actors targeting customer networks.  • Monitor and analyze DoD and open-source intelligence feeds to identify Indicators of Compromise (IOCs) and integrate them into security sensors and SIEMs.  • Report security incidents to customers and USCYBERCOM, ensuring timely communication and coordinated response. 
Required Qualifications:  • Minimum active DoD Secret clearance with the ability to obtain TS/SCI.  • Current DoD 8570 IAT Level II certification (or higher), such as CompTIA Security+ CE, ISC2 SSCP, or SANS GSEC (or equivalent).  • Ability to obtain DoD 8570 CSSP-A Level Certification (e.g., CEH, CySA+, GCIA, or equivalent) within 180 days of hire.  • Strong foundation in networking, including packet analysis, common ports and protocols, and traffic flow. Knowledge of the OSI model, defense-in-depth security principles, and common security elements for effective threat detection, analysis, and mitigation as a SOC Security Analyst.  • Education and experience requirements:  ◦ Level I: Bachelor's degree and 1+ years of relevant experience; equivalent work experience and/or military service may be considered in lieu of a degree.  • Proven ability to work effectively both independently and as a collaborative team member, demonstrating initiative and a strong work ethic in both settings.   • Committed to continuous learning and self-improvement in the cybersecurity domain, as evidenced by ongoing pursuit of certifications, active participation in industry forums, and dedication to staying ahead of emerging threats and technologies.  • Excellent problem-solving skills, including the ability to collaborate effectively with cross-functional teams to address complex security challenges in real-world scenarios. This includes the ability to communicate technical information clearly and concisely, build consensus, and drive solutions to completion.   • Reliable and flexible, with a demonstrated willingness to work assigned shifts to support operational requirements and team objectives.  • Located within a commutable distance (within 2 hours) or able to self-relocate to Hill AFB, UT; Scott AFB, IL; or Columbus, OH. 
Preferred Qualifications:  • Hands-on experience analyzing large volumes of logs, network data (e.g., Netflow, Full Packet Capture), and other attack artifacts during incident investigations.  • In-depth experience using a SIEM/SOAR platform to analyze multiple log types and events across various data points, applying techniques such as behavioral analysis, statistical analysis, and machine learning to detect and respond to advanced threats.   • Comprehensive understanding of the network threat lifecycle, attack vectors, and methods of exploitation, including intrusion set tactics, techniques, and procedures (TTPs).  • Experience with Anti-Virus, HIPS/HBSS, IDS/IPS, Full Packet Capture, and Network Forensics tools.  • Experience or knowledge in monitoring, defending, or administering cloud networks (e.g., AWS, Azure, GCP), including cloud-native security tools and strategies for protecting data in cloud environments. Experience identifying and mitigating cloud-specific attacks.   • Experience managing, defending, administering, or deploying mobile devices (iOS, Android) for enterprise, including mobile device management (MDM), mobile application management (MAM), and mobile threat defense (MTD). A strong understanding of mobile security best practices and mobile threat landscape is highly desired.   • Scripting and programming skills.