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Mobile Machine Learning Jobs in New Mexico (NOW HIRING)

Computational neuroscience, neuro-informatics, and machine learning Researchers working in related areas that leverage advanced neuroimaging, computational methods, mobile MRI, or multidisciplinary ...

Computational neuroscience, neuro-informatics, and machine learning Researchers working in related areas that leverage advanced neuroimaging, computational methods, mobile MRI, or multidisciplinary ...

Appraiser

Albuquerque, NM · On-site

$22.81 - $36.01/hr

Conduct commercial and residential appraisals of land, real property, mobile homes, and business ... telefax machine. * Vehicle is used on a frequent basis in traveling from property to property.

Appraiser

Albuquerque, NM · On-site

$22.81 - $36.01/hr

Conduct commercial and residential appraisals of land, real property, mobile homes, and business ... telefax machine. * Vehicle is used on a frequent basis in traveling from property to property.

Mobile Machine Learning information

See New Mexico salary details

$11

$24

$115

How much do mobile machine learning jobs pay per hour?

As of Jul 28, 2026, the average hourly pay for mobile machine learning in New Mexico is $24.54, according to ZipRecruiter salary data. Most workers in this role earn between $13.99 and $19.57 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 New Mexico? The most popular types of Machine Learning jobs in New Mexico are:
What job categories do people searching Mobile Machine Learning jobs in New Mexico look for? The top searched job categories for Mobile Machine Learning jobs in New Mexico are:
What cities in New Mexico are hiring for Mobile Machine Learning jobs? Cities in New Mexico with the most Mobile Machine Learning job openings:
Senior Data Scientist - Digital Intelligence, Device Signals

Senior Data Scientist - Digital Intelligence, Device Signals

Socure

Miami, NM • On-site

Full-time

Posted 27 days ago


Job description

Job Summary:
Socure is building the identity trust infrastructure for the digital economy, and they are seeking a Senior Data Scientist to join their Digital Intelligence team. In this role, you will drive the development of machine learning features and models to power fraud prevention and identity verification using device, network, and behavioral data.
Responsibilities:
• Design and deploy advanced machine learning systems for device identification, anomaly detection, and fraud prevention—balancing precision, recall, and real-world adversarial dynamics.
• Contribute to the development of scalable data pipelines and production ML workflows using structured and unstructured telemetry (e.g., browser, mobile, session data).
• Investigate high-complexity signals (e.g., emulator use, spoofing, low-entropy fingerprints), applying advanced statistical methods and domain knowledge to detect fraud and abuse.
• Translate ambiguous business problems into modeling approaches, using a combination of supervised, unsupervised, and heuristic techniques.
• Partner with engineering, product, and risk teams to contribute to data architecture decisions, signal collection, and planning.
• Drive experimental design, A/B testing frameworks, and robust validation techniques to ensure model generalizability and long-term trust.
• Contribute to team standards for ML explainability, risk evaluation, and feature logging.
• Document methodologies and communicate results effectively through dashboards, presentations, and reports for both technical and executive audiences.
• Mentor junior data scientists and participate in cross-functional working groups.
Qualifications:
Required:
• Master’s degree (or equivalent practical experience) in Computer Science, Machine Learning, Statistics, or a related quantitative field.
• 6+ years of experience in data science or applied machine learning, including experience working in production environments.
• Excellent SQL skills and extensive experience with large-scale databases and data modeling.
• Proven track record of deploying and maintaining ML models in live systems, ideally involving streaming or near-real-time data.
• Proficiency in Python and distributed computing tools (e.g., Spark, PySpark).
• Hands-on experience with ML frameworks such as scikit-learn, XGBoost, TensorFlow, or similar.
• Excellent communication skills—able to explain complex technical results to non-technical stakeholders and senior leadership.
• Experience designing and interpreting experiments, working with real-world noisy datasets, and applying sound validation techniques to assess model robustness.
• Demonstrated ability to break down ambiguous problems, apply analytical rigor, and uncover meaningful insights that influence product or risk strategies.
• Strong judgment across data quality, model selection, and business impact tradeoffs.
• Collaborative mindset and experience working cross-functionally with product, engineering, and analytics teams.
Preferred:
• Background in fraud detection, behavioral biometrics, anomaly detection, or adversarial modeling.
• Experience with high-cardinality feature engineering techniques (e.g., frequency/target encoding, embeddings).
• Familiarity with privacy-preserving or robust ML techniques.
• Knowledge of browser/mobile fingerprinting, VPN/proxy detection, or telemetry signal processing.
Company:
Socure is a predictive analytics platform for digital identity verification of consumers. Founded in 2012, the company is headquartered in Incline Village, USA, with a team of 501-1000 employees. The company is currently Late Stage.