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Mobile Machine Learning Jobs in Georgia (NOW HIRING)

AGS

Machine Learning Engineer

Duluth, GA · On-site

Gaming, mobile gaming, or consumer behavioral analytics experience * Familiarity with casino game ... Machine learning breadth - classification, regression, clustering, recommendation systems; can ...

Infinite Resource Solutions

Senior Developer

Alpharetta, GA · On-site

$53.25 - $70.25/hr

We build a wide variety of solutions from automated infrastructure and cloud-based systems, to machine learning and user experiences for web, mobile and IoT. We're looking for people who want to join ...

Shoptalk

Director Software Development

Alpharetta, GA · On-site

$190 - $260/hr

Any time you swipe your credit card, pay through a mobile app, or withdraw money from the bank, w ... Define architecture and technology strategy for backend services, data platforms, machine learning ...

Dolby Laboratories, Inc.

Data Research Engineer

Atlanta, GA · On-site

$110K - $132K/yr

... mobile computing, computer networking, and IoT. What You Will Accomplish Sight and Sound play a ... Develop tools and frameworks to enable scalable development of machine learning models and data ...

Dolby

Sr. Data Researcher Engineer

Atlanta, GA · On-site

... mobile computing, computer networking, and IoT. What You Will Accomplish Sight and Sound play a ... Develop tools and frameworks to enable scalable development of machine learning models and data ...

Dolby

Data Research Engineer

Atlanta, GA · On-site

$110K - $132K/yr

... mobile computing, computer networking, and IoT. What You Will Accomplish Sight and Sound play a ... Develop tools and frameworks to enable scalable development of machine learning models and data ...

Monitise

Senior Data Scientist

Alpharetta, GA

Any time you swipe your credit card, pay through a mobile app, or withdraw money from the bank, w ... Develop machine learning models, scoring approaches, and analytical methods that support MOA, Offer ...

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Mobile Machine Learning information

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as senior machine learning engineer or AI research director, often requiring advanced skills in programming, data analysis, and deep learning. These roles usually involve leadership responsibilities, specialized knowledge, and may be found in large tech companies or research institutions.

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.

Will MLE be replaced by AI?

Mobile Machine Learning Engineers (MLEs) develop and optimize machine learning models for mobile devices. While AI technologies continue to advance, MLEs focus on implementing efficient, lightweight models suitable for mobile hardware, and their role is expected to evolve rather than be fully replaced by AI itself. Skills in model optimization, deployment, and understanding mobile constraints remain essential for MLEs.

What engineer makes $500,000 a year?

Senior machine learning engineers, including those working on mobile applications, can earn $500,000 or more annually, especially with extensive experience, advanced skills in deep learning and AI, and roles in high-paying industries or companies. Achieving this level often requires advanced degrees, specialized expertise, and leadership responsibilities.

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.

Which 3 jobs will survive AI?

Mobile Machine Learning professionals, data scientists, and AI system engineers are likely to continue thriving as AI advances, due to their expertise in developing, managing, and interpreting complex models. These roles require specialized skills in programming, statistics, and domain knowledge, making them less susceptible to automation. Continuous learning and staying updated with AI tools and frameworks are essential for long-term job security in this field.

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 are the most commonly searched types of Machine Learning jobs in Georgia? The most popular types of Machine Learning jobs in Georgia are:
What are popular job titles related to Mobile Machine Learning jobs in Georgia? For Mobile Machine Learning jobs in Georgia, the most frequently searched job titles are:
What job categories do people searching Mobile Machine Learning jobs in Georgia look for? The top searched job categories for Mobile Machine Learning jobs in Georgia are:
What cities in Georgia are hiring for Mobile Machine Learning jobs? Cities in Georgia with the most Mobile Machine Learning job openings:
Mobile Machine Learning jobs near you
Infographic showing various Mobile Machine Learning job openings in Georgia as of July 2026, with employment types broken down into 1% As Needed, 74% Full Time, 23% Part Time, 1% Temporary, and 1% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution.

Machine Learning Engineer

AGS

Duluth, GA • On-site

Full-time

Posted 7 days ago

Job description


Job Overview
The Data Scientist / ML Engineer builds and deploys predictive models and analytical systems that turn AGS's player and game data into quantitative insights that directly improve game design and commercial decisions. This role bridges behavioral data science (understanding how players interact with games) and production ML engineering (deploying models that actually reach decision-makers). It feeds game designers with data-driven design recommendations for the ML-driven game design initiative, supports yield management with predictive models for Interactive YieldMax, and enables operators to understand their player base more deeply - anchored to AGS's Tech & Data hero mission of an accessible data layer with live KPIs powering every decision.
Responsibilities
  • Build player session behavioral models - retention prediction, abandonment modeling, post-bonus behavior analysis, and bet escalation modeling from iGaming session data
  • Develop game performance prediction models - predict WPUPD, time on device, and floor longevity from game specification features and historical performance data, using a game feature extraction pipeline that reverse-engineers existing titles into structured, reusable features
  • Build math model optimization analytics - analyze actual vs. theoretical RTP, hit frequency, and bonus frequency; identify math model anomalies across the deployed fleet
  • Create player segmentation models - cluster players into behavioral archetypes (bonus hunters, jackpot chasers, base game grinders) to inform game design and operator recommendations
  • Support the Interactive YieldMax yield-management tool - build the underlying models that predict which AGS game maximizes performance in a given floor position, operator property, and player demographic
  • Build predictive maintenance models - analyze cabinet error logs and, as sensor/telemetry pipelines mature (Dynamics Field Service / Dataverse), incorporate telemetry to identify failure precursor patterns and predict component failures
  • Feed game design decisions - translate model outputs into game designer-friendly insights that are actionable in the game specification process
  • Design and analyze A/B tests - experimental design, statistical analysis, and results interpretation for game math variant testing (where regulatorily permitted)
  • Productionalize models - package models for deployment on Azure ML/Fabric, with MLflow-based registry, monitoring, and retraining pipelines

Skills/Requirements
  • 4-8 years of data science and/or ML engineering experience, with demonstrated production model deployment (not just notebook analysis)
  • Behavioral analytics expertise - has built retention, churn, or engagement models using event-level behavioral data (session logs, clickstreams, transaction sequences)
  • Strong Python and SQL skills - pandas, scikit-learn, XGBoost, statsmodels; can query the data warehouse independently (a mix of on-prem SQL Server and Salesforce today, migrating to Microsoft Fabric/OneLake) without relying on a data engineer for every analysis
  • Statistical rigor - survival analysis, A/B test design, causal inference, regression modeling; understands the difference between correlation and causation
  • Machine learning breadth - classification, regression, clustering, recommendation systems; can select the right modeling approach for each problem
  • Data communication skills - can translate model outputs into business-friendly language that game designers and commercial leaders can act on
  • Experience with messy, real-world data - comfortable where game features aren't fully documented and pipelines are still being built; doesn't require perfect data to deliver value
  • Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Mathematics, or related quantitative field

Preferred
  • Gaming, mobile gaming, or consumer behavioral analytics experience
  • Familiarity with casino game mechanics - RTP, volatility, Hold & Spin, theo index
  • Experience with time series analysis and anomaly detection for IoT/sensor data
  • Knowledge of responsible gambling data considerations
  • Experience with MLflow, Azure ML, or Fabric Notebooks/Spark for model lifecycle management

Note: All offers are contingent upon successful completion of a background check
*Posted positions are not open to third party recruiters and unsolicited resume submissions will be considered free referrals.
AGS is an equal opportunity employer
Job Snapshot
  • Location:
    Atlanta, Georgia
    Duluth, Georgia
  • Job Type:
    Executive/HR/IT
  • Date Posted:
    07/20/2026

About Us
AGS is a global company focused on creating a diverse mix of entertaining gaming experiences for every kind of player. Our roots are firmly planted in the Class II Native American gaming market, and our customer-centric culture and growth have helped us branch out to become a leading all-inclusive commercial gaming supplier. Powered by high-performing Class II and Class III slot products, an expansive table products portfolio, real-money gaming platforms and content, highly rated social casino solutions for operators and players, and best-in-class service, we offer an unmatched value proposition for our casino partners. Learn more at www.playags.com.
Job Snapshot
  • Location:
    Atlanta, Georgia
    Duluth, Georgia
  • Job Type:
    Executive/HR/IT
  • Date Posted:
    07/20/2026

Job Description
Job Overview
The Data Scientist / ML Engineer builds and deploys predictive models and analytical systems that turn AGS's player and game data into quantitative insights that directly improve game design and commercial decisions. This role bridges behavioral data science (understanding how players interact with games) and production ML engineering (deploying models that actually reach decision-makers). It feeds game designers with data-driven design recommendations for the ML-driven game design initiative, supports yield management with predictive models for Interactive YieldMax, and enables operators to understand their player base more deeply - anchored to AGS's Tech & Data hero mission of an accessible data layer with live KPIs powering every decision.
Responsibilities
  • Build player session behavioral models - retention prediction, abandonment modeling, post-bonus behavior analysis, and bet escalation modeling from iGaming session data
  • Develop game performance prediction models - predict WPUPD, time on device, and floor longevity from game specification features and historical performance data, using a game feature extraction pipeline that reverse-engineers existing titles into structured, reusable features
  • Build math model optimization analytics - analyze actual vs. theoretical RTP, hit frequency, and bonus frequency; identify math model anomalies across the deployed fleet
  • Create player segmentation models - cluster players into behavioral archetypes (bonus hunters, jackpot chasers, base game grinders) to inform game design and operator recommendations
  • Support the Interactive YieldMax yield-management tool - build the underlying models that predict which AGS game maximizes performance in a given floor position, operator property, and player demographic
  • Build predictive maintenance models - analyze cabinet error logs and, as sensor/telemetry pipelines mature (Dynamics Field Service / Dataverse), incorporate telemetry to identify failure precursor patterns and predict component failures
  • Feed game design decisions - translate model outputs into game designer-friendly insights that are actionable in the game specification process
  • Design and analyze A/B tests - experimental design, statistical analysis, and results interpretation for game math variant testing (where regulatorily permitted)
  • Productionalize models - package models for deployment on Azure ML/Fabric, with MLflow-based registry, monitoring, and retraining pipelines

Skills/Requirements
  • 4-8 years of data science and/or ML engineering experience, with demonstrated production model deployment (not just notebook analysis)
  • Behavioral analytics expertise - has built retention, churn, or engagement models using event-level behavioral data (session logs, clickstreams, transaction sequences)
  • Strong Python and SQL skills - pandas, scikit-learn, XGBoost, statsmodels; can query the data warehouse independently (a mix of on-prem SQL Server and Salesforce today, migrating to Microsoft Fabric/OneLake) without relying on a data engineer for every analysis
  • Statistical rigor - survival analysis, A/B test design, causal inference, regression modeling; understands the difference between correlation and causation
  • Machine learning breadth - classification, regression, clustering, recommendation systems; can select the right modeling approach for each problem
  • Data communication skills - can translate model outputs into business-friendly language that game designers and commercial leaders can act on
  • Experience with messy, real-world data - comfortable where game features aren't fully documented and pipelines are still being built; doesn't require perfect data to deliver value
  • Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Mathematics, or related quantitative field

Preferred
  • Gaming, mobile gaming, or consumer behavioral analytics experience
  • Familiarity with casino game mechanics - RTP, volatility, Hold & Spin, theo index
  • Experience with time series analysis and anomaly detection for IoT/sensor data
  • Knowledge of responsible gambling data considerations
  • Experience with MLflow, Azure ML, or Fabric Notebooks/Spark for model lifecycle management

Note: All offers are contingent upon successful completion of a background check
*Posted positions are not open to third party recruiters and unsolicited resume submissions will be considered free referrals.
AGS is an equal opportunity employer
About Us
AGS is a global company focused on creating a diverse mix of entertaining gaming experiences for every kind of player. Our roots are firmly planted in the Class II Native American gaming market, and our customer-centric culture and growth have helped us branch out to become a leading all-inclusive commercial gaming supplier. Powered by high-performing Class II and Class III slot products, an expansive table products portfolio, real-money gaming platforms and content, highly rated social casino solutions for operators and players, and best-in-class service, we offer an unmatched value proposition for our casino partners. Learn more at www.playags.com.

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