ZipRecruiter

Log In

1

Embedded Machine Learning Jobs in Washington (NOW HIRING)

MORSE Corp

Machine Learning Engineer

Arlington, VA · On-site

We are seeking a Machine Learning Engineer to join our team at MORSE. You will play a pivotal role ... embedded system. You will be part of our team working to accelerate our US National Security ...

Heven AeroTech

Machine Learning Engineer

Sterling, VA · On-site

Machine Learning Engineer Company: Heven AeroTech Location: Sterling, Virginia FLSA: Exempt About ... Embedded Linux and ROS experience * Defense/aerospace industry background * Additional Google Cloud ...

MORSE Corp

Machine Learning Engineer

Arlington, VA · On-site

$90K - $210K/yr

We are seeking a Machine Learning Engineer to join our team at MORSE. You will play a pivotal role ... embedded system. You will be part of our team working to accelerate our US National Security ...

AppFolio

Sr. Machine Learning Engineer

Washington, DC · Remote

$118.40K - $162.50K/yr

Assistant : a GenAI copilot embedded across the product experience * Flows: an agentic workflow ... Who we are looking for We're seeking a Sr Machine Learning Engineer to play a critical role in ...

next page

Showing results 1-20

People also search for

All JobsEmbedded Machine Learning JobsEmbedded Machine Learning Jobs in Washington

Embedded Machine Learning information

See Washington salary details

$79.3K

$173.7K

$197.1K

How much do embedded machine learning jobs pay per year?

As of May 29, 2026, the average yearly pay for embedded machine learning in Washington is $173,722.00, according to ZipRecruiter salary data. Most workers in this role earn between $148,900.00 and $195,900.00 per year, depending on experience, location, and employer.

What is an Embedded Machine Learning job?

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 the Embedded Machine Learning position, and why are they important?

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.
More about Embedded Machine Learning jobs
What are the most commonly searched types of Embedded Machine Learning jobs in Washington? The most popular types of Embedded Machine Learning jobs in Washington are:
What are popular job titles related to Embedded Machine Learning jobs in Washington? For Embedded Machine Learning jobs in Washington, the most frequently searched job titles are:
What job categories do people searching Embedded Machine Learning jobs in Washington look for? The top searched job categories for Embedded Machine Learning jobs in Washington are:
Embedded Machine Learning jobs near you
Machine Learning Engineer

Machine Learning Engineer

MORSE Corp

Arlington, VA • On-site

Other

Posted 24 days ago

Job description

We are seeking a Machine Learning Engineer to join our team at MORSE. You will play a pivotal role in designing, implementing, and managing complex ML algorithms and systems, with a focus on computer vision (CV) and other types of data. You will be responsible for acquiring truth data, integrating algorithms, testing algorithms, combining algorithms, reviewing literature to stay on top of the latest-and-greatest methods, analyzing data from field tests, and developing advanced algorithms. MORSE's AI & ML work crosses modalities, and experience or interest in the fields of Large Language Models (LLM), audio analysis, computer vision, and advanced reasoning is a plus. You will work with MORSE's current team of engineers to transition algorithms to production, which may run on on-prem servers, on the cloud, or on a real-time embedded system. You will be part of our team working to accelerate our US National Security customers abilities to use natural language processing capabilities in mission-critical environments. 

Responsibilities: 
  • Develop, fine-tune, train, and optimize Computer Vision algorithms processing tasks such as object detection and tracking.  
  • Use MLOps tools for efficient experiment tracking, data management, and reproducibility 
  • Write robust, efficient, and maintainable code 
  • Track the latest advancements with machine learning research to bring new techniques and methodologies to MORSE 
  • Conduct experiments and perform rigorous evaluations to assess the effectiveness and efficiency of CV models 

Skills and Requirements: 
  • US CITIZENSHIP REQUIRED and the ability to obtain a U.S. Security Clearance 
  • Masters or Ph.D. in Computer Science, Computer Engineering, Data Science, Aerospace, Mathematics, Physics, or related field 
  • Proven experience in applying CV models, techniques, frameworks, and libraries to implement and fine-tune models 
  • Proven experience testing and validating the performance of AI technologies in real-world applications 
  • Proficiency in Python 
  • Experience with cloud platforms (AWS and Azure) 
  • Experience with Docker 
  • Experience with MLOps tools such as Airflow, MLFlow, AimStack, etc. 
  • Exceptional communication skills and the ability to work well with customers 
  • Understanding of Department of Defense requirements and standards is a plus 

Apply