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Mobile Machine Learning Jobs in Massachusetts (NOW HIRING)

They are seeking a highly motivated, product-oriented Machine Learning Scientist to join their core ... mobile environments. • Background in wireless localization or radar signal processing • ...

Your Impact We are seeking highly skilled and innovative Machine Learning Scientists to join our AI ... Experience in developing computer vision algorithms for resource-constrained devices such as mobile ...

Senior Machine Learning Scientist

Boston, MA · On-site

$99K - $135K/yr

Your Impact We are seeking highly skilled and innovative Machine Learning Scientists to join our AI ... Experience in developing computer vision algorithms for resource-constrained devices such as mobile ...

... deep learning requests from mobile devices. * Assist with analysis of the development of the wound analysis algorithms using deep learning using photographs and thermal images, and on machine ...

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Mobile Machine Learning information

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How much do mobile machine learning jobs pay per hour?

As of Jul 4, 2026, the average hourly pay for mobile machine learning in Massachusetts is $27.66, according to ZipRecruiter salary data. Most workers in this role earn between $15.77 and $22.07 per hour, depending on experience, location, and employer.

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 Massachusetts? The most popular types of Machine Learning jobs in Massachusetts are:
What cities in Massachusetts are hiring for Mobile Machine Learning jobs? Cities in Massachusetts with the most Mobile Machine Learning job openings:
Principal Machine Learning Engineer, Foundation Models

Principal Machine Learning Engineer, Foundation Models

Cambridge Mobile Telematics

Cambridge, MA • On-site

Full-time

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


Job description

Job Summary:
Cambridge Mobile Telematics (CMT) is the world’s largest telematics and AI company for safer mobility. As a Principal Machine Learning Engineer on the DriveWell Atlas team, you will lead the development of next-generation AIs focused on telematics data to enhance risk assessment and driver engagement.
Responsibilities:
• Use independent judgment and discretion to lead the design, pre-training, fine-tuning, and deployment of novel foundation models for vehicle telematics
• Develop and implement novel algorithms for modeling both automotive physics and human driving behavior
• Pioneer advanced self-supervised learning techniques, including the design and implementation of innovative tasks tailored to multi-modal telematics sensor data to learn rich representations of movement and driver behavior
• Develop models robust to noise, missing data, and diverse operating conditions typical of real-world mobile sensor and IoT datasets
• Build and manage scalable training and inference pipelines using tools like Ray, PyTorch DDP, Horovod, or similar frameworks
• Integrate these AIs into production systems while ensuring high performance and reliability
• Optimize these AIs for efficient deployment on various platforms, including cloud and edge/mobile devices
• Collaborate closely with engineering, product, and research teams to translate cutting-edge research into impactful products and features for the DriveWell Atlas platform
• Mentor junior scientists and contribute to the broader AI/ML strategy at CMT
• Stay abreast of the latest AI advancements, evaluating and adopting emerging technologies and methodologies relevant to telematics
• Contribute to efforts in AI explainability and interpretability
• Complete any tasks as they arise
Qualifications:
Required:
• Bachelor’s degree or equivalent years of experience and/or certification in Artificial Intelligence, Computer Science, Electrical Engineering, Physics, Mathematics, Statistics, or a related field
• 7+ years of professional experience in AI/ML
• 3+ years of hands-on experience developing and deploying foundation models, with a strong portfolio in generative AI for sequential or spatio-temporal data
• Strong, hands-on experience in building and training time-series transformer architectures for complex sensor fusion and behavioral modeling tasks is required
• Deep expertise in designing pretraining tasks for self-supervised learning on noisy, real-world sensor data
• Proficiency in Python and common data science libraries (e.g., Pandas, NumPy, scikit-learn)
• Extensive experience with deep learning frameworks such as PyTorch (preferred) or TensorFlow for large-scale model training and deployment
• Solid understanding and practical experience with distributed training techniques and efficient training methodologies for large models
• Experience building and maintaining large-scale data processing pipelines and machine learning infrastructure using tools like Spark, Airflow, Docker, and cloud platforms (e.g., AWS, GCP, Azure)
• Excellent problem-solving skills and the ability to translate complex business problems into tractable AI-based solutions
• Strong verbal/written communication and collaboration skills, with the ability to effectively convey complex technical concepts to diverse audiences
• Product-focused thinking with a proven ability to deliver impactful AI solutions
Preferred:
• PhD or Master's degree preferred
• Experience with MLOps practices and tools for managing the lifecycle of machine learning models
• Publications in top-tier AI/ML conferences or journals
• Familiarity with techniques for model interpretability and explainability (XAI)
• Awareness of ethical AI principles, bias detection, and mitigation strategies in machine learning models, including experience with or understanding of model guardrails
Company:
Cambridge Mobile Telematics develops DriveWell, a complete telematics and behavioral analytics solution to improve safety. Founded in 2010, the company is headquartered in Cambridge, USA, with a team of 201-500 employees. The company is currently Growth Stage.