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Mobile Machine Learning Jobs in Tennessee (NOW HIRING)

... machine capability rates * Assist in the identification, implementation and evaluation of lean ... Continuous Learning * Performance Leadership * Demonstrates Participative Management Style, Builds ...

As a Heavy Duty Mechanic at Drax, you will conduct routine maintenance on machines to ensure the ... A Supportive Team: Work in an environment where continuous learning is encouraged, and your ...

As a Heavy Duty Mechanic at Drax, you will conduct routine maintenance on machines to ensure the ... A Supportive Team: Work in an environment where continuous learning is encouraged, and your ...

As a Heavy Duty Mechanic at Drax, you will conduct routine maintenance on machines to ensure the ... A Supportive Team: Work in an environment where continuous learning is encouraged, and your ...

Heavy Duty Mechanic

Memphis, TN · On-site

$56.82/hr

As a Heavy Duty Mechanic at Drax, you will conduct routine maintenance on machines to ensure the ... A Supportive Team: Work in an environment where continuous learning is encouraged, and your ...

Inbound Fulfillment Leader

Jackson, TN · On-site

$88.70K - $139.26K/yr

Develop, execute and scale strategy to utilize autonomous mobile robot (AMR) fleets to deliver and ... Focused on continuous learning and continuous improvement. * Proficient in MS Office and/or related ...

HVAC Service Technician III

Chattanooga, TN · On-site

$31.60 - $50.37/hr

Perform maintenance of commercial HVAC equipment including logging machines, cleaning coils ... Participate in available training through regional technician trainers, off-site learning modules ...

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Mobile Machine Learning information

See Tennessee salary details

$11

$22

$108

How much do mobile machine learning jobs pay per hour?

As of May 31, 2026, the average hourly pay for mobile machine learning in Tennessee is $22.99, according to ZipRecruiter salary data. Most workers in this role earn between $13.08 and $18.32 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 Tennessee? The most popular types of Machine Learning jobs in Tennessee are:
What cities in Tennessee are hiring for Mobile Machine Learning jobs? Cities in Tennessee with the most Mobile Machine Learning job openings:
Associate/Full Professor of Digital Health, Fall 2027

Associate/Full Professor of Digital Health, Fall 2027

The University of Tennessee

Knoxville, TN • On-site

Full-time

This job post has expired today. Applications are no longer accepted.


Job description

Job Description
The College of Education, Health, and Human Sciences (CEHHS) at The University of Tennessee, Knoxville (UTK) is seeking applicants for a tenure-track associate or full professor of digital health to join the Department of Nutrition and Public Health Sciences (NPHS) for a nine-month position beginning August 1, 2027. Digital health generally involves advanced information and communication technologies (e.g., online and mobile applications, wearable sensors, computing platforms, connectivity, software, and machine learning/AI) to assess, intervene upon, and optimize human health behaviors and clinical outcomes. The successful candidate will be a nationally recognized scholar in the field and will hold a Gary and Becki Blauser endowed professorship position that provides additional support for high-quality scholarship. We seek a preeminent scholar who will contribute to the advancement of the department's reputation in research and who will provide leadership in research and training in the area of digital health. Additionally, under the leadership of Dr. J. Graham Thomas, UTK is launching the Center for Applied Digital Health and Optimization Methods, which will enhance the university's national footprint in digital health and optimization methods. This position will help support interdisciplinary collaborations in digital health within the center, across the campus, and within the UT system.
Responsibilities
Duties and responsibilities, with a focus in the area of digital health as appropriate, include:
• Sustaining a nationally recognized, high-impact research agenda demonstrated through multiple indicators, such as publications in top-tier peer-reviewed journals, acquisition of competitive external funding, invited grant reviewer for federal funding, invited presentations, and recognition by professional associations.
• Enhancing UTK's ability to be a leader in digital health research demonstrated by supporting digital health research within the UT system and collaborating with health-focused industry and community partners to engage in impactful research.
• Providing mentorship and research guidance for NPHS Ph.D. students, fostering their scholarly productivity and professional development.
• Mentoring junior faculty to support their scholarly productivity, external funding success, and professional advancement.
• Supporting graduate programs in NPHS through high-quality teaching, research-informed advising, and curriculum innovation.
• Engaging actively in professional associations at the local, state, regional, and national/international levels, including presenting research, holding leadership positions, and contributing to the advancement of the field.
• Providing meaningful service contributions to the department, college, university, and broader educational and professional communities.
• Collaborating with faculty, staff, and students to advance interdisciplinary research, program development, and collective scholarly initiatives.
• Demonstrating a strong commitment to the research, educational, and service missions of the university, college, and department.
Qualifications
Required qualifications include:
• Earned doctorate in psychology, public health, epidemiology, biostatistics, computer science, engineering, informatics, or a related discipline in the social, behavioral, or technical sciences.
• Evidence of teaching effectiveness in graduate-level programs, as demonstrated by course evaluations, teaching awards, course syllabi, or documented instructional innovations.
• Documented record of interdisciplinary collaboration, as demonstrated through co-authored publications, interdisciplinary projects, or service/leadership roles.
• Evidence of engagement, as demonstrated through partnerships with industry, health-focused community-partners, or other engaged partners.
Required qualifications for Associate Professors include:
• Meet the qualifications for appointment as an Associate Professor as defined in the University of Tennessee, Knoxville's Faculty Handbook and the NPHS bylaws (e.g., evidence of sustained contributions in research, teaching, and service).
• Established research agenda and record of scholarly productivity as demonstrated by peer-reviewed publications, book chapters, monographs, invited presentations, editorships, or leadership roles in professional associations.
• Evidence of success in securing external funding, such as serving as principal investigator (PI), co-PI, or in other senior personnel roles on funded grants/contracts, receiving fellowships, or contributing substantively to externally supported projects.
• Evidence of mentoring doctoral and/or postdoctoral students documented by co-authored publications and dissertation supervision or serving as a mentor on a postdoctoral fellowship or career development award.
Required qualifications for Full Professors include:
• Meet the qualifications for appointment as a Full Professor as defined in the University of Tennessee, Knoxville's Faculty Handbook and the NPHS bylaws (e.g., distinguished record of research, teaching, and service).
• Extensive record of scholarly productivity as demonstrated by a sustained pattern of peer-reviewed publications, books or edited volumes, invited keynote addresses, leadership roles in professional associations, and other indicators of national/international recognition.
• Extensive record of securing external funding, including documented experience serving as principal investigator (PI) or lead investigator on major federal, foundation, or state grants/contracts, with evidence of effective funded research project management.
• Documented record of mentoring doctoral and/or postdoctoral students and junior faculty toward successful scholarly careers, as evidenced by co-authored publications, dissertation supervision or serving as a mentor on a postdoctoral fellowship or career development award, external grant collaborations, and the professional placement/advancement of mentees.
Preferred qualifications include:
• Experience with distance learning and innovative teaching methodologies, including hybrid and online course design and delivery.
• Demonstrated ability to work collegially and collaboratively with faculty, staff, students, and community partners.
• Demonstrated record of leadership within their department or college, and through active leadership roles in professional associations at the regional, national, and international levels.
• Expertise in one or more technical aspects of digital health research (e.g., ecological momentary assessment, wearable sensors, machine learning/AI, electronic health record integration, geospatial tracking, virtual/augmented reality).