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Embedded Machine Learning Internship Jobs in Richardson, TX

Deloitte

Cyber - SecOps - Consultant

Dallas, TX · On-site

... machine learning to address cyber-specific challenges. Cyber Detect & Respond practitioners work with clients to modernize large-scale cyber data and analytics programs, support embedded and as-a ...

Deloitte

Cyber - SecOps - Consultant

Fort Worth, TX

... machine learning to address cyber-specific challenges. Cyber Detect & Respond practitioners work with clients to modernize large-scale cyber data and analytics programs, support embedded and as-a ...

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All JobsEmbedded Machine Learning JobsEmbedded Machine Learning Internship JobsEmbedded Machine Learning Internship Jobs in Richardson, TX

Embedded Machine Learning Internship information

See Richardson, TX salary details

$23.2K

$38.7K

$79.9K

How much do embedded machine learning internship jobs pay per year?

As of Aug 7, 2026, the average yearly pay for embedded machine learning internship in Richardson, TX is $38,671.00, according to ZipRecruiter salary data. Most workers in this role earn between $29,500.00 and $41,800.00 per year, depending on experience, location, and employer.

What is an embedded machine learning internship?

An Embedded Machine Learning Internship is a temporary position designed for students or recent graduates to gain hands-on experience in developing and deploying machine learning algorithms on embedded systems. These internships typically involve working with hardware such as microcontrollers, sensors, or edge devices, and using specialized tools to optimize machine learning models for low-power and resource-constrained environments. Interns collaborate with engineers and data scientists to create efficient, real-world AI solutions that run directly on devices rather than relying on cloud computing. This role helps bridge the gap between theoretical machine learning concepts and practical implementation on embedded platforms.

What are some typical projects or tasks I might work on during an embedded machine learning internship?

During an Embedded Machine Learning Internship, you can expect to work on projects such as optimizing machine learning models to run efficiently on hardware with limited resources, integrating AI algorithms into embedded systems (like microcontrollers or IoT devices), and performing real-time data processing. You'll likely collaborate closely with software engineers and hardware designers to test models on physical devices, debug performance issues, and contribute to documentation. These experiences provide practical exposure to the challenges of deploying AI in real-world, resource-constrained environments and help build skills valuable for a future career in embedded AI.

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

To thrive as an Embedded Machine Learning Intern, you need a background in computer science, electrical engineering, or a related field with strong programming skills in C/C++ and Python, as well as foundational knowledge of machine learning algorithms. Experience with embedded systems development tools (such as ARM Cortex, Raspberry Pi, or Arduino), version control systems, and familiarity with ML frameworks like TensorFlow Lite or Edge Impulse is often required. Analytical thinking, problem-solving ability, and effective teamwork are vital soft skills for success in this role. These skills and qualities are crucial for efficiently developing, optimizing, and deploying machine learning solutions on resource-constrained embedded platforms.
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Embedded Machine Learning Internship jobs near you

Senior Staff Software Engineer, Perception (R4985)

Shield AI

Dallas, TX • On-site

$233K - $350K/yr

Full-time

Posted 10 days ago

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 develop and deploy advanced machine learning models that enable autonomous systems to better understand, reason about, and interact with their environment. You'll partner closely with machine learning researchers, autonomy engineers, perception engineers, and platform teams to translate emerging AI capabilities into reliable, production-ready systems for U.S. and international defense customers. This is an ideal opportunity for engineers who enjoy building state-of-the-art AI systems while solving the practical challenges of deploying them on operational autonomous platforms. 
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 10 years of related experience with a Bachelor’s degree; or 9 years and a Master’s degree; or 7 years with a PhD; or equivalent work experience.

  • Mastery 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).

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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.