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Mobile Machine Learning Jobs (NOW HIRING)

PROPRIUS

Machine Learning / Data Scientist

Bodega Bay, CA · On-site

$110K - $140K/yr

Machine Learning Our client is a digital invention agency focused on machine learning methodologies, enterprise mobile and web applications, eCommerce, augmented reality and IoT. They look to ...

Apple

Sr. Machine Learning Engineer

Santa Clara, CA

$171.60K - $258.10K/yr

... mobile devices to search and find what they are looking for. As part of our team, you will be ... in machine learning and software engineering to understand user queries and intents, retrieve and ...

Apple

Sr. Machine Learning Engineer

Santa Clara, CA

$171.60K - $258.10K/yr

... mobile devices to search and find what they are looking for. As part of our team, you will be ... in machine learning and software engineering to understand user queries and intents, retrieve and ...

TikTok

Machine Learning Engineer, Commerce Ads

San Jose, CA · On-site

$156K - $316.80K/yr

We are seeking Machine Learning Engineers who can help us to improve our existing delivery system ... About TikTok TikTok is the leading destination for short-form mobile video. At TikTok, our mission ...

Qualcomm

Machine Learning Researcher

San Diego, CA · On-site

... teams across mobile, compute, automotive, cloud, and IOT. We also perform and publish ... optimizing machine learning models, systems, platforms, or methods. Qualcomm is an equal ...

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How much do mobile machine learning jobs pay per hour?

As of May 29, 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

Machine Learning / Data Scientist

PROPRIUS

Bodega Bay, CA • On-site

$110K - $140K/yr

Full-time

Posted 11 days ago

Job description

Machine Learning Engineer

Location: San Francisco, CA

Sponsorship: No

Relocation: No

Industry: Machine Learning

Our client is a digital invention agency focused on machine learning methodologies, enterprise mobile and web applications, eCommerce, augmented reality and IoT. They look to innovatively make this world a better place with each and every product, system, idea and app they release.

Job Summary

Our client is looking for a machine learning engineer to join our existing ML team in developing and refining a predictive application.

The ideal candidate is adept at using large data sets to find opportunities for product and process optimization and using models to test the effectiveness of different courses of action.

You must have strong experience using a variety of data mining/data analysis methods, using a variety of data tools, building and implementing models, using/creating algorithms and creating/running simulations. You must have a proven ability to drive business results with their data-based insights. You must be comfortable working with a wide range of stakeholders and functional teams. The right candidate will have a passion for discovering solutions hidden in large data sets and working with stakeholders to improve business outcomes.

As a ML Engineer, you will:

  • Work with stakeholders throughout the organization to identify opportunities for leveraging data to drive business solutions
  • Mine and analyze data from databases to drive optimization and improvement of product development, marketing techniques and business strategies
  • Assess the effectiveness and accuracy of new data sources and data gathering techniques
  • Develop custom data models and algorithms to apply to data sets
  • Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting and other business outcomes
  • Coordinate with different functional teams to implement models and monitor outcomes
  • Develop processes and tools to monitor and analyze model performance and data accuracy

For this role you will need:

  • Strong with Statistics and can code in either R, Python, Java and Scala
  • Experience with designing and building using micro-services architectural pattern, web APIs using dotnet core & C#
  • Experience and passion for simulations, optimization, neural networks, artificial intelligence (deep learning and machine learning)
  • Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, GIT, SQL, etc.
  • Able to understand statistical solutions and execute similar activities
  • Experience in data wrangling and advanced analytic modeling
  • Strong communication and organizational skills and has the ability to deal with ambiguity while juggling multiple priorities and projects at the same time
  • Experience visualizing/presenting data for stakeholders using: Seaborn, Business Objects, D3, ggplot, etc.
  • Ability to investigate the feasibility and data requirements necessary to develop an ML solution for a given problem
  • Ability to design, build and test production ready ML-based products while interpreting and explaining the basis for predictions generated by ML models

The perfect candidate will have:

  • Knowledge and experience using one or more of the following, or similar, machine learning software frameworks: CAFFE, Torch 7, Keras and Tensorflow
  • Experience building production-ready NLP or information retrieval systems
  • Hands-on experience with NLP tools, libraries and corpora (e.g. NLTK, Stanford CoreNLP, Wikipedia corpus, etc.)

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