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Hourly Embedded Machine Learning Jobs in Texas (NOW HIRING)

Polluxa, Inc.

Embedded AI/ML Developer - HP

Spring, TX · On-site

$125 - $150/hr

Description - The Embedded AI/ML Developer will design, develop, and optimize AI-enabled embedded ... This role focuses on deploying efficient machine learning models at the edge, integrating AI ...

SuperMom's LLC

Machine Learning Engineer II

Irving, TX · On-site

As a Machine Learning Engineer II at 7-Eleven, you will collaborate with cross-functional teams and ... If an hourly or salary range is included in this ad it represents the range 7-Eleven in good faith ...

7-ELEVEN

Machine Learning Engineer II

Irving, TX · On-site

As a Machine Learning Engineer II at 7-Eleven, you will collaborate with cross-functional teams and ... If an hourly or salary range is included in this ad it represents the range 7-Eleven in good faith ...

7-Eleven

Machine Learning Engineer II

Irving, TX · On-site

As a Machine Learning Engineer II at 7-Eleven, you will collaborate with cross-functional teams and ... If an hourly or salary range is included in this ad it represents the range 7-Eleven in good faith ...

Hp

Embedded AI/ML Developer

Spring, TX · On-site

$117K - $154K/yr

Embedded AI/ML Developer Description - The Embedded AI/ML Developer will design, develop, and ... This role focuses on deploying efficient machine learning models at the edge, integrating AI ...

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Hourly Embedded Machine Learning information

What is an hourly embedded machine learning engineer?

An Hourly Embedded Machine Learning engineer is a professional who specializes in developing and deploying machine learning models on embedded systems, such as microcontrollers, IoT devices, or edge devices, and is compensated on an hourly basis rather than a salaried or project-based arrangement. These engineers work to optimize algorithms so they can run efficiently on devices with limited computing power, memory, and energy resources. Their responsibilities often include model selection, quantization, optimization, and integration of machine learning pipelines into hardware. Hiring on an hourly basis allows for flexibility in project scope and duration, making it ideal for companies with specific, time-limited needs. They often collaborate with hardware engineers, data scientists, and software developers to create intelligent embedded solutions.

How does an hourly embedded machine learning professional typically collaborate with hardware and software teams during a project?

As an Hourly Embedded Machine Learning professional, you will often work closely with both hardware and software engineering teams to ensure that machine learning models are efficiently integrated into embedded systems. This typically involves frequent communication to align on hardware constraints, such as memory and processing power, and to optimize algorithms for real-time performance. You may also participate in joint debugging sessions and code reviews to address integration issues and streamline deployment. Collaboration is key, as successful projects depend on the seamless interaction between machine learning solutions and the embedded hardware platform.

What are the key skills and qualifications needed to thrive as an hourly embedded machine learning engineer, and why are they important?

To thrive as an Hourly 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 engineering or a related field. Familiarity with tools such as TensorFlow Lite, embedded Linux, microcontroller development environments, and model optimization frameworks is typically required. Strong problem-solving skills, adaptability, and effective communication help you address complex technical challenges and collaborate with cross-functional teams. These skills are crucial for designing efficient, real-time ML solutions that operate reliably on resource-constrained embedded devices.

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

AspectHourly Embedded Machine LearningHourly Data Scientist
CredentialsKnowledge of embedded systems, programming, ML algorithmsDegree in Data Science, Statistics, or related field
Work EnvironmentEmbedded hardware, IoT devices, real-time systemsData analysis, modeling, visualization in office or cloud
Industry UsageConsumer electronics, automotive, IoT devicesFinance, healthcare, marketing, research

Hourly Embedded Machine Learning specialists focus on integrating ML models into embedded systems and hardware, often working with IoT devices and real-time constraints. In contrast, Hourly Data Scientists analyze large datasets to develop predictive models primarily in cloud or office environments. While both roles require programming skills, embedded ML emphasizes hardware integration, whereas data science centers on data analysis and visualization.

What are popular job titles related to Hourly Embedded Machine Learning jobs in Texas?

For Hourly Embedded Machine Learning jobs in Texas, the most frequently searched job titles are:

What job categories do people searching Hourly Embedded Machine Learning jobs in Texas look for?

The top searched job categories for Hourly Embedded Machine Learning jobs in Texas are:

What cities in Texas are hiring for Hourly Embedded Machine Learning jobs?

Cities in Texas with the most Hourly Embedded Machine Learning job openings:

Hourly Embedded Machine Learning jobs near you
Infographic showing various Hourly Embedded Machine Learning job openings in Texas as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 75% Full Time, 21% Part Time, and 2% Contract. Highlights an 82% Physical, 3% Hybrid, and 15% Remote job distribution.

Embedded AI/ML Developer - HP

Spring, TX • On-site

Polluxa, Inc.
See more jobs at Polluxa, Inc.external

$125 - $150/hr

Other

This job post has expired 2 days ago. Applications are no longer accepted.

Job description

Description -

The Embedded AI/ML Developer will design, develop, and optimize AI-enabled embedded software solutions for HP's commercial PC and connected device portfolio. This role focuses on deploying efficient machine learning models at the edge, integrating AI capabilities with firmware and system software, and enabling intelligent user experiences across resource-constrained platforms.

The engineer will work closely with hardware, firmware, software, and data science teams to translate AI/ML concepts into production-ready embedded implementations. Responsibilities include model optimization, inference runtime integration, performance tuning, debugging, documentation, and staying current with emerging edge AI technologies, tools, and industry best practices.

Responsibilities
  • Designs, develops, and optimizes embedded AI/ML software for edge devices, including PCs, docking solutions, displays, peripherals, and other intelligent client platforms.
  • Converts AI/ML algorithms and proof-of-concept models into efficient, production-quality embedded implementations optimized for latency, memory, power, and compute constraints.
  • Integrates machine learning inference engines, model runtimes, and AI accelerators into embedded firmware and system software environments.
  • Collaborates with cross-functional teams to define AI feature requirements, system architecture, data flow, model deployment strategy, and validation plans.
  • Profiles and tunes embedded AI workloads to improve inference performance, reduce memory footprint, improve responsiveness, and optimize power consumption.
  • Develops and maintains software interfaces between AI/ML components, firmware, device drivers, sensors, embedded controllers, and host applications.
  • Supports model compression, quantization, pruning, benchmarking, and deployment using embedded AI frameworks and hardware acceleration technologies.
  • Troubleshoots complex system-level issues involving AI inference, firmware behavior, sensor data, device communication, and platform integration.
  • Creates and maintains technical documentation, including architecture descriptions, design specifications, model deployment guides, validation procedures, and integration notes.
Education & Experience Recommended
  • Bachelor's or Master's degree in Computer Science, Computer Engineering, Statistics, Mathematics, Artificial Intelligence, Machine Learning, Robotics or a related technical discipline.
Preferred Certifications

Embedded AI/ML Engineering

  • Hands-on experience deploying AI/ML models on embedded systems, edge devices, MCUs, SoCs, NPUs, DSPs, or other constrained compute platforms.
  • Experience with model optimization techniques such as quantization, pruning, compression, TensorRT, ONNX, TFLite, or similar deployment toolchains.
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