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Remote Embedded Machine Learning Jobs in Texas (NOW HIRING)

MedReview

Data Scientist

Austin, TX · On-site +1

As a part of our team, you will leverage your analytical skills and expertise in machine learning ... However, for the right fit, we may consider remote . Responsibilities: * Problem Identification:

BNSF Railway

Sr/Staff Data Scientist (Remote - US)

TX · On-site +1

$165K - $300K/yr

REMOTE Anticipated Start Date: 07/01/2026 The US base salary range for this full-time position is ... Lead the development and deployment of advanced machine learning models to forecast outcomes and ...

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All JobsEmbedded Machine Learning JobsRemote Embedded Machine Learning JobsRemote Embedded Machine Learning Jobs in Texas

Remote Embedded Machine Learning information

What are the key skills and qualifications needed to thrive as a Remote Embedded Machine Learning Engineer, and why are they important?

To thrive as a Remote Embedded Machine Learning Engineer, you need a solid background in embedded systems, machine learning algorithms, and programming languages like C/C++ and Python, often supported by a degree in computer science, electrical engineering, or related fields. Familiarity with microcontrollers, edge AI frameworks (such as TensorFlow Lite or Edge Impulse), and version control systems is typically required. Strong problem-solving skills, effective communication, and self-motivation are essential soft skills for collaborating remotely and troubleshooting complex issues. These skills ensure successful deployment of intelligent solutions on resource-constrained devices and effective teamwork in distributed environments.

What is a Remote Embedded Machine Learning Engineer?

A Remote Embedded Machine Learning Engineer is a professional who develops and deploys machine learning models on embedded systems like microcontrollers, IoT devices, and edge hardware, all while working remotely. Their work involves optimizing algorithms to run efficiently on devices with limited computing power, memory, and battery life. These engineers typically use frameworks such as TensorFlow Lite or TinyML to design intelligent features that operate directly on hardware, enabling real-time decision-making without relying heavily on cloud connectivity. They collaborate with cross-functional teams and often troubleshoot both software and hardware issues from a remote location.

What is the difference between Remote Embedded Machine Learning vs Remote Data Scientist?

AspectRemote Embedded Machine LearningRemote Data Scientist
Required CredentialsBachelor's or Master's in Computer Science, Electrical Engineering, or related fields; experience with embedded systems and ML frameworksBachelor's or Master's in Data Science, Statistics, or related fields; proficiency in data analysis and ML algorithms
Work EnvironmentEmbedded hardware devices, IoT systems, real-time processing environmentsCloud platforms, data analysis labs, remote offices
Employer & Industry UsageTech companies, IoT device manufacturers, automotive, roboticsFinance, healthcare, marketing, tech firms

Remote Embedded Machine Learning specialists focus on integrating ML models into embedded hardware for real-time applications, often working with IoT and robotics. In contrast, Remote Data Scientists analyze large datasets to extract insights, primarily working in cloud or office environments. Both roles require strong analytical skills but differ in technical focus and work settings.

What are some common challenges faced by Remote Embedded Machine Learning Engineers, and how can they be addressed?

Remote Embedded Machine Learning Engineers often encounter challenges related to hardware access, debugging embedded devices remotely, and collaborating with cross-functional teams across time zones. To address these, it's important to set up robust remote development environments, use simulation tools when physical hardware isn't available, and establish clear communication channels for effective teamwork. Regular virtual meetings and detailed documentation also help ensure alignment and smooth progress, despite the remote nature of the work.
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What cities in Texas are hiring for Remote Embedded Machine Learning jobs? Cities in Texas with the most Remote Embedded Machine Learning job openings:
Remote Embedded Machine Learning jobs near you
Software Engineer - AI Specialist | Remote

Software Engineer - AI Specialist | Remote

GigWorld Talent Solutions

Dallas, TX • On-site, Remote

Other

Posted 28 days ago

Job description

Job Description Software Engineer - AI Specialist | Remote Permanent, Full-Time We are supporting an innovative technology firm dedicated to building AI-driven solutions that drive efficiency and transformation across industries. We are seeking a highly skilled Software Engineer specializing in Artificial Intelligence to develop, optimize, and deploy AI-powered applications. Responsibilities: Design, develop, and implement AI models and machine learning algorithms.

Collaborate with cross-functional teams to integrate AI solutions into existing platforms. Optimize AI models for performance, scalability, and efficiency. Research and apply the latest advancements in AI and deep learning.

Develop and maintain data pipelines and AI-driven analytics systems. Ensure AI models are robust, ethical, and aligned with best practices. Troubleshoot and improve AI-based applications as needed.

Stay updated on emerging AI trends and technologies. Requirements: Bachelor's or Master's degree in Computer Science, Engineering, or a related field. Proven experience in AI and machine learning development.

Proficiency in programming languages such as Python, Java, or C++. Strong understanding of AI frameworks and libraries (TensorFlow, PyTorch, Scikit-learn, etc.). Experience with natural language processing (NLP), computer vision, or predictive analytics

Knowledge of data science methodologies and model evaluation techniques. Experience with cloud platforms and AI services (AWS, Azure, GCP). Ability to work independently and collaboratively in a fast-paced environment.

Preferred Qualifications: Experience with reinforcement learning and generative AI models. Familiarity with AI ethics, bias mitigation, and explainability techniques. Contribution to open-source AI projects.

Understanding of big data technologies and distributed computing. Benefits: Competitive salary and performance-based incentives. Flexible work schedule and remote work opportunities.

Professional development and continuous learning resources. Opportunity to work with a passionate and innovative team in a fast-growing industry.

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