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Embedded Machine Learning Internship Jobs in Washington

Capital One

Sr. Lead Machine Learning Engineer

Mclean, VA · On-site +1

$103K - $136K/yr

Sr. Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE) , you'll be ... Internship experience does not apply) * At least 4 years of experience programming with Python ...

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Embedded Machine Learning Internship information

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
Embedded Software Engineer - Edge ML/Low SWaP Systems - R138

Embedded Software Engineer - Edge ML/Low SWaP Systems - R138

Expedition Technology

Herndon, VA • On-site

$135K - $177K/yr

Other

This job post has expired today. Applications are no longer accepted.

Job description

About EXP
At Expedition Technology (EXP), we solve the nation's toughest defense and intelligence challenges through advanced analytics, machine learning, and software engineering. Our teams work at the intersection of mission and innovation, delivering impactful capabilities in real-world environments.

About the Role
We are seeking an Embedded Software Engineer to support the deployment of advanced data processing and machine learning solutions to low size, weight, and power (SWaP) systems. This role focuses on optimizing and deploying algorithms to GPU-enabled embedded platforms (e.g., NVIDIA Jetson) for real-time applications.

What You'll Do
* Deploy and optimize computer vision, signal processing, or data processing algorithms on embedded hardware
* Improve real-time, low-latency performance of ML pipelines on constrained systems
* Profile CPU/GPU performance and identify system bottlenecks
* Collaborate on algorithm selection based on hardware constraints
* Containerize and deploy solutions using tools like Docker
* Work in Linux-based environments and contribute to production-quality code
* Partner with ML and software engineers to transition models into operational environments

Required Qualifications
* United States Citizenship - for US Government security clearance eligibility
* Active Top Secret/SCI (TS/SCI) security clearance
* Experience deploying software or algorithms to embedded or edge systems
* Proficiency in Python or ability to learn quickly
* Experience working in Linux environments
* Experience optimizing performance in constrained environments
* Strong problem-solving skills across software and hardware domains

Preferred Qualifications
* Experience with NVIDIA Jetson or similar GPU-enabled embedded platforms
* Experience with video or signal processing pipelines
* Familiarity with CUDA and CPU/GPU profiling
* Experience with Docker or containerization
* Experience with ML frameworks such as PyTorch or ONNX
* Prior experience working on ML-focused teams

Additional Information
This role is focused on embedded systems with GPU acceleration rather than traditional microcontroller or FPGA-centric work. Candidates should be comfortable working across the full lifecycle of algorithm development and deployment in performance-sensitive environments.

Work Environment & Culture
At EXP, we value collaboration, continuous learning, and innovation. Engineers are encouraged to leverage modern tools and technologies, including AI-assisted development, while working closely with teammates to solve challenging mission problems.


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