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Mobile Machine Learning Jobs (NOW HIRING)

Qualcomm

Machine Learning Engineer

San Diego, CA · On-site

As a Machine Learning Engineer, you will develop and implement machine learning techniques and collaborate with cross-functional teams to enhance mobile, edge, auto, and IoT products.

Qualcomm

Machine Learning Engineer

San Diego, CA · On-site

$122.80K - $184.20K/yr

Qualcomm Engineers collaborate with cross-functional teams to enhance the world of mobile, edge, auto, and IOT products through machine learning hardware and software. Minimum Qualifications: • ...

Qualcomm

Machine Learning Engineer

San Diego, CA · On-site

$122.80K - $184.20K/yr

Qualcomm Engineers collaborate with cross-functional teams to enhance the world of mobile, edge, auto, and IOT products through machine learning hardware and software. Minimum Qualifications: • ...

Navstar

Machine Learning Engineer

Centreville, VA

$102K - $144.38K/yr

Machine Learning Engineer II The Machine Learning Engineer II will be a member of the Learning and ... mobile sensor platforms with the primary purpose of real-time automated target recognition (ATR)

Jobs for Humanity

Machine Learning Engineers

San Jose, CA · On-site

$194K - $355K/yr

M1758 Responsibilities TikTok is the leading destination for short-form mobile video. At TikTok ... machine learning algorithms and experienced in federated learning frameworks and applications ...

Rivet Industries

Machine Learning Researcher

San Jose, CA · On-site

$150K - $290K/yr

Machine Learning Researcher Location: 2550 N First Street Suite 250, San Jose, California 95131 ... Optimize models for real-time performance on mobile and embedded platforms * Implement MLOps best ...

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How much do mobile machine learning jobs pay per hour?

As of Jun 3, 2026, the average hourly pay for mobile machine learning in the United States is $25.32, according to ZipRecruiter salary data. Most workers in this role earn between $14.42 and $20.19 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.

More about Mobile Machine Learning jobs
What cities are hiring for Mobile Machine Learning jobs? Cities with the most Mobile Machine Learning job openings:
What are the most commonly searched types of Machine Learning jobs? The most popular types of Machine Learning jobs are:
What states have the most Mobile Machine Learning jobs? States with the most job openings for Mobile Machine Learning jobs include:
What job categories do people searching Mobile Machine Learning jobs look for? The top searched job categories for Mobile Machine Learning jobs are:
Mobile Machine Learning jobs near you
Infographic showing various Mobile Machine Learning job openings in the United States as of May 2026, with employment types broken down into 1% Internship, 38% Full Time, 57% Part Time, 3% Contract, and 1% Nights. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $52,676 per year, or $25.3 per hour.

Machine Learning Engineer

RZR Global Inc.

San Francisco, CA • On-site

Full-time

Posted 14 days ago

Job description

Who are we?RZR Global is an AI-driven company specializing in mobile advertising solutions designed to fuel revenue growth. We leverage AI to discover audiences in a privacy-first environment through trillions of contextual bidding signals and proprietary behavioral models. Our audience engagement platform includes creative strategy and execution. We handle 5 million mobile ad requests per second from over 10 billion devices, driving performance for both publishers and brands. We are headquartered in San Francisco, CA, with a global presence across the United States, EMEA, and APAC.
The role?
We are seeking a motivated and detail-oriented Machine Learning Engineer to join our team. As an ML Engineer, you will be involved in designing and implementing machine learning models and data pipelines to enhance our programmatic demand-side platform (DSP). You will work closely with Senior MLE and other team members to drive impactful machine learning projects and contribute to innovative solutions.
What will you do?
  • Support the development of machine learning models to address challenges in programmatic advertising, such as predicting user responses, forecasting bid landscapes, and detecting fraud.
  • Collaborate with senior data scientists and cross-functional teams (product, engineering, and analytics) to integrate models into production workflows.
  • Analyze the impact of integrating new data sources and features into our models.
  • Build and maintain data pipelines to process and prepare large datasets for model training and evaluation.
  • Contribute ideas and assist in testing new tools, methodologies, and technologies to improve our machine learning capabilities.
  • Document experiments, assumptions, and outcomes; maintain reproducibility
What are we looking for?
  • Bachelor's degree in Mathematics, Physics, Computer Science, or a related technical field.
  • At least 2 years of professional experience in machine learning, statistical analysis, and data analysis.
  • Experience with machine learning techniques such as regression, classification, and clustering.
  • Proficiency in Python and SQL and familiarity with big data tools (e.g., Spark) and ML libraries (e.g., TensorFlow, PyTorch, Scikit-Learn).
  • Strong grasp of probability, statistics, and data analysis principles.
  • Ability to work effectively in a team environment, with good communication skills to explain complex concepts to diverse stakeholders.
Nice-to-Have
  • Familiarity with system programming languages including C++ and Rust is a plus.
  • Exposure to online inference systems, gRPC/REST model endpoints, or streaming features (Kafka/Flink)
  • Ad-tech familiarity: auction dynamics, pacing, fraud signals, creative personalization.

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