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Embedded Machine Learning Jobs in Dallas, TX (NOW HIRING)

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Embedded Machine Learning information

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$69.2K

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How much do embedded machine learning jobs pay per year?

As of Sep 15, 2026, the average yearly pay for embedded machine learning in Dallas, TX is $151,732.00, according to ZipRecruiter salary data. Most workers in this role earn between $130,100.00 and $171,100.00 per year, depending on experience, location, and employer.

What is an embedded machine learning?

An Embedded Machine Learning job involves developing and optimizing machine learning models to run efficiently on resource-constrained devices like microcontrollers, edge devices, and IoT hardware. Professionals in this role work on model compression, low-power inference, and real-time processing, ensuring AI capabilities can function without relying on cloud computing. Responsibilities often include data preprocessing, feature extraction, model training, and deployment on embedded systems using frameworks like TensorFlow Lite or Edge Impulse.

What are the key skills and qualifications needed to thrive in embedded machine learning?

To thrive in Embedded Machine Learning, you should have expertise in machine learning algorithms, embedded systems programming (e.g., C/C++, Python), and a solid understanding of hardware-software integration, typically backed by a degree in computer engineering, electrical engineering, or a related field. Familiarity with edge AI tools (such as TensorFlow Lite, ONNX, or Edge Impulse), microcontrollers, and real-time operating systems is highly valued, alongside relevant certifications such as Embedded Systems or AI certificates. Strong problem-solving skills, effective communication, and the ability to work cross-functionally are crucial soft skills in this field. These qualifications and qualities are vital for creating efficient, reliable AI solutions that operate seamlessly within resource-constrained environments and interdisciplinary project teams.

What are some common challenges faced by professionals working in embedded machine learning roles?

Professionals in embedded machine learning roles often face the challenge of optimizing machine learning models to run efficiently on resource-constrained hardware, such as microcontrollers or edge devices with limited memory and processing power. Balancing model accuracy, inference speed, and energy consumption can require creative problem-solving and deep knowledge of both hardware and software. Additionally, collaboration with hardware engineers, data scientists, and software developers is key, as projects typically require cross-functional teamwork to meet performance and deployment goals. Staying current with rapidly evolving tools and best practices is also important in this dynamic field.

What are popular job titles related to Embedded Machine Learning jobs in Dallas, TX?

For Embedded Machine Learning jobs in Dallas, TX, the most frequently searched job titles are:

What job categories do people searching Embedded Machine Learning jobs in Dallas, TX look for?

The top searched job categories for Embedded Machine Learning jobs in Dallas, TX are:

Embedded Machine Learning jobs near you
Infographic showing various Embedded Machine Learning job openings in Dallas, TX as of September 2026, with employment types broken down into 49% Internship, and 51% Contract. Highlights an 100% In-person job distribution, with an average salary of $151,732 per year, or $72.9 per hour.

Staff Software Engineer, Perception (R5421)

Dallas, TX

Shield AI
Software Development • 11 - 50 employees
Learn more about Shield AIexternal

$195K - $292K/yr

Full-time

Re-posted 18 days ago

Key responsibilities

  • Design, train, fine-tune, and maintain vision, vision-language, and vision-language-action models to improve perception and decision-making for autonomous systems.

  • Build scalable data pipelines, supervised fine-tuning workflows, and evaluation frameworks to enhance model performance on mission-relevant tasks.

  • Deploy and optimize machine learning models for embedded hardware using technologies such as ONNX, TensorRT, and hardware-accelerated inference frameworks.

Job description

Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube. 

The Hivemind Solutions Perception team develops the next generation of perception capabilities for autonomous systems by combining state-of-the-art machine learning with the proven foundations of computer vision. The team advances how autonomous platforms understand and interpret the world by developing vision, vision-language (VLM), and vision-language-action (VLA) models that tackle core perception challenges such as object understanding, scene interpretation, and mission-relevant environmental awareness. Working at the intersection of research and production, our engineers build the data pipelines, supervised fine-tuning (SFT) workflows, evaluation frameworks, and deployment infrastructure needed to transform cutting-edge AI research into reliable, mission-ready perception capabilities.
 
In this role, you'll lead the technical development of advanced machine learning solutions that define the future of perception for autonomous systems. You'll own the team's most challenging technical problems, drive architecture and model development across multiple efforts, and influence how foundation models are adapted, evaluated, and deployed for real-world autonomy. Working closely with researchers, perception engineers, autonomy engineers, and platform teams, you'll bridge cutting-edge AI research with scalable production systems while mentoring engineers and raising the technical bar across the organization.
What You'll Do:

Model Development – Design, train, fine-tune, and maintain state-of-the-art vision, vision-language, and vision-language-action models that improve perception and decision-making for autonomous systems. 

Data Pipelines & Model Training – Build scalable data pipelines, supervised fine-tuning (SFT) workflows, and evaluation loops that continuously improve model performance on mission-relevant tasks. 

Model Deployment & Optimization – Deploy and optimize machine learning models for embedded hardware using technologies such as ONNX, TensorRT, and hardware-accelerated inference frameworks. 

Perception & Autonomy Applications – Apply modern machine learning techniques to solve challenging perception and autonomy problems across aerial and other autonomous systems operating in complex, real-world environments. 

Research-to-Production – Translate cutting-edge machine learning research into production-ready capabilities by balancing model performance, robustness, computational efficiency, and operational reliability. 

Cross-functional Collaboration – Partner closely with perception, autonomy, platform, and software engineering teams to integrate machine learning capabilities into mission-ready autonomous systems. 

Model Evaluation & Validation – Develop benchmarks, testing methodologies, and evaluation frameworks to measure model performance, identify failure modes, and guide future improvements. 

Continuous Improvement – Improve training infrastructure, developer tooling, deployment workflows, and model lifecycle management to accelerate experimentation and production delivery. 

Required Qualifications:

  • Typically requires a minimum of 7 years of related experience with a Bachelor’s degree; or 6 years and a Master’s degree; or 4 years with a PhD; or equivalent work experience.

  • Expertise of machine learning fundamentals. 

  • Experience training an deploying ML models for computer vision in a production setting. 

  • Strong understanding of 3D vision problems/algorithms. 

  • Experience with machine learning frameworks such as PyTorch and TensorFlow. 

  • Demonstrated expertise in deploying models using TensorRT and ONNX. 

  • Proficiency in C++ and Python. 

  • Strong analytical and problem-solving skills, with the ability to translate research into practical applications. 

  • Ability to obtain a SECRET clearance 
Preferred Qualifications:

  • Experience with developing autonomous systems for defense customers. 

  • Experience with training/finetuning vision-language models, vision-language-action models, and/or world models.  

  • Contributions to open-source projects in machine learning or computer vision. 

  • Track record of publications in leading computer vision and robotics conferences and journals (e.g., CVPR, ICCV/ECCV, RAL, ICRA).

#LI-DS-1
#LD

Full-time regular employee offer package:
Pay within range listed + Bonus + Benefits + Equity
 
Temporary employee offer package:
Pay within range listed above + temporary benefits package (applicable after 60 days of employment)
 
Salary compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. All offers are contingent on a cleared background and possible reference check. Military fellows and part-time employees are not eligible for benefits. Please speak to your talent acquisition representative for more information.
 
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Shield AI is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please let us know. 

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.