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Embedded Machine Learning Jobs in Washington (NOW HIRING)

Parsons

Machine Learning / Data Scientist

Washington, DC

$88K - $154K/yr

The Machine Learning / Data Scientist is a hands-on practitioner with strong capabilities in model ... Familiarity with integrating model outputs into BI tools or applications (e.g., via APIs, embedded ...

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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 Jun 19, 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.

Will AI replace embedded programmers?

Embedded machine learning involves developing algorithms for resource-constrained devices, and while AI tools can assist with coding and optimization, embedded programmers are essential for designing, implementing, and maintaining these systems. AI is more likely to augment their work rather than fully replace them, especially given the need for specialized knowledge of hardware and real-time constraints.

Is embedded AI a good career?

Embedded machine learning involves developing AI models for hardware with limited resources, such as IoT devices and embedded systems. It is a growing field with demand for skills in hardware programming, C/C++, and AI frameworks, offering opportunities in industries like automotive, healthcare, and consumer electronics.

Is embedded systems still a good career in 2026?

Embedded Machine Learning remains a strong career in 2026 as industries increasingly adopt AI-powered devices and IoT solutions. Professionals with skills in hardware programming, real-time systems, and machine learning frameworks like TensorFlow Lite are in demand for developing intelligent embedded applications. Continuous learning and familiarity with microcontrollers, sensors, and embedded software development are essential for long-term growth in this field.

What engineers make $500,000?

Senior engineers in specialized fields such as embedded machine learning, AI, or data science can reach salaries of $500,000 or more, especially with extensive experience, advanced skills in programming and hardware, and leadership roles. High compensation often involves working in high-demand industries, with additional bonuses or stock options contributing to total earnings.

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 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 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
Infographic showing various Embedded Machine Learning job openings in Washington as of June 2026, with employment types broken down into 86% Full Time, 2% Part Time, 4% Temporary, 4% Contract, and 4% Nights. Highlights an 88% Physical, 1% Hybrid, and 11% Remote job distribution, with an average salary of $173,722 per year, or $83.5 per hour.

Sr. Staff Embedded AI Engineer

Renesas Electronics

Columbia, MD

$130K - $171K/yr

Full-time

Posted 24 days ago

Job description

Company Description

Renesas is seeking a Sr. Staff Embedded AI Engineer to develop advanced TinyML and embedded AI solutions targeting Renesas microcontroller and MPU platforms (RA, RL78, RX, RZ). This is a highly technical, hands-on role focused on building cloud-based model translation infrastructure and optimizing network inference for resource-constrained embedded systems. You will contribute to a small team developing a service that converts trained machine learning models into efficient C/C++ implementations for deployment on microcontrollers. The ideal candidate combines strong embedded software expertise with solid machine learning fundamentals and is comfortable working across the stack — from neural network internals to low-level performance optimization. You should be someone who contributes new ideas, challenges assumptions, and helps improve both tooling and embedded implementation quality

Job Description
  • BS/MS/PhD in Electrical Engineering, Computer Engineering, Computer Science, or related field. 
  • 6+ years of experience in embedded systems software development.
  • Strong proficiency in C/C++ for embedded platforms. 
  • Strong proficiency in Python for tooling, automation, or ML workflows. 
  • Experience deploying machine learning models to resource-constrained systems. 
  • Solid understanding of neural network fundamentals and internals
  • Experience with machine learning frameworks such as TensorFlow or PyTorch.
  • Experience optimizing performance, memory footprint, and power consumption on embedded targets.
Qualifications

• Experience developing inference runtimes, model translation tools, or code generation systems.
• Experience with CMSIS-NN or other embedded ML acceleration libraries.
• Experience optimizing quantized neural networks for embedded systems using SIMD/DSP acceleration.
• Familiarity with Renesas MCU/MPU platforms (RA, RL78, RX, RZ).
• Experience with real-time systems (RTOS or bare-metal).
• Hardware debugging experience.


Additional Information

Renesas is an embedded semiconductor solution provider driven by its Purpose ‘To Make Our Lives Easier.’ As the industry’s leading expert in embedded processing with unmatched quality and system-level know-how, we have evolved to provide scalable and comprehensive semiconductor solutions for automotive, industrial, infrastructure, and IoT industries based on the broadest product portfolio, including High Performance Computing, Embedded Processing, Analog & Connectivity, and Power.
With a diverse team of over 22,000 professionals in more than 30 countries, we continue to expand our boundaries to offer enhanced user experiences through digitalization and usher into a new era of innovation. We design and develop sustainable, power-efficient solutions today that help people and communities thrive tomorrow, ‘To Make Our Lives Easier.’     
At Renesas, you can: 

  • Launch and advance your career in technical and business roles across four Product Groups and various corporate functions. You will have the opportunities to explore our hardware and software capabilities and try new things.  
  • Make a real impact by developing innovative products and solutions to meet our global customers' evolving needs and help make people’s lives easier, safe and secure. 
  • Maximize your performance and wellbeing in our flexible and inclusive work environment. Our people-first culture and global support system, including the remote work option and Employee Resource Groups, will help you excel from the first day.    

Are you ready to own your success and make your mark?  

Join Renesas. Shape Your Future with Us.  

Renesas Electronics is an equal opportunity and affirmative action employer, committed to celebrating diversity and fostering a work environment free of discrimination on the basis of sex, race, religion, national origin, gender, gender identity, gender expression, age, sexual orientation, military status, veteran status, or any other basis protected by federal, state or local law. For more information, please read our Diversity & Inclusion Statement.

Renesas Electronics deals with dual-use technology that is subject to U.S. export controls regulations. Under these regulations it may be necessary for Renesas to obtain U.S. government export license prior to release of technology to certain persons. The decision whether or not to file or pursue an export license application is at the sole discretion of Renesas.

We have adopted a hybrid model that gives employees the ability to work remotely two days a week while ensuring that we come together as a team in the office the rest of the time. The designated in-office days are Tuesday through Thursday for innovation, collaboration and continuous learning.