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Embedded Machine Learning Engineer Jobs in Washington

10a Labs

Machine Learning Engineer

Washington, DC · On-site

$130K - $200K/yr

We are seeking a Machine Learning Engineer (3-5+ years of experience) to help design, build, evaluate, and deploy advanced machine learning systems across a range of safety, security, and ...

Ametek

Machine Learning Engineer

Herndon, VA · Hybrid

We are seeking an earlycareer Machine Learning Engineer who is excited to grow rapidly by building and deploying productiongrade ML systems. The ideal candidate has a strong engineering mindset, has ...

Ametek

Machine Learning Engineer

Herndon, VA · On-site

We are seeking an early-career Machine Learning Engineer who is excited to grow rapidly by building and deploying production-grade ML systems. The ideal candidate has a strong engineering mindset ...

Xometry

Staff Machine Learning Engineer

North Bethesda, MD · On-site

$200K - $220K/yr

... Xometry's embedded DFM AI + IQE integration with Teamcenter and Designcenter. You will be ... machine learning engineering, with a track record of owning and delivering complex ML systems in ...

Capital One

Lead Machine Learning Engineer

Mclean, VA · On-site +1

$103K - $136K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You ...

Kitware

Machine Learning Engineer

Arlington, VA · On-site

$110K - $160K/yr

... machine learning, artificial intelligence, and computer vision * Perform rapid prototyping and enhanced development to be integrated into operational systems * Contribute your strong programming ...

Capital One

Lead Machine Learning Engineer

Mclean, VA · On-site +1

$103K - $136K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You ...

Kitware

Machine Learning Engineer

Arlington, VA · Hybrid

$110K - $160K/yr

... machine learning, artificial intelligence, and computer vision * Perform rapid prototyping and enhanced development to be integrated into operational systems * Contribute your strong programming ...

Kitware

Machine Learning Engineer

Arlington, VA · Hybrid

$110K - $160K/yr

... machine learning, artificial intelligence, and computer vision * Perform rapid prototyping and enhanced development to be integrated into operational systems * Contribute your strong programming ...

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 part of an Agile team dedicated to productionizing machine learning applications and systems at scale.

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All JobsEmbedded Machine Learning Engineer JobsEmbedded Machine Learning Engineer Jobs in Washington

Embedded Machine Learning Engineer information

See Washington salary details

$79.3K

$173.7K

$197.1K

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

As of Jul 9, 2026, the average yearly pay for embedded machine learning engineer 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 are the key skills and qualifications needed to thrive as an Embedded Machine Learning Engineer, and why are they important?

To thrive as an Embedded Machine Learning Engineer, you need expertise in machine learning algorithms, embedded systems programming (C/C++ or Python), and a solid understanding of hardware constraints, usually supported by a degree in computer science, electrical engineering, or related fields. Familiarity with tools like TensorFlow Lite, ONNX, microcontroller SDKs, and experience with real-time operating systems (RTOS) are typically required. Strong problem-solving, communication skills, and the ability to collaborate across multidisciplinary teams help you stand out in this role. These skills are crucial for efficiently deploying intelligent models on resource-constrained devices, ensuring optimal performance and seamless integration in real-world applications.

What does an Embedded Machine Learning Engineer do?

An Embedded Machine Learning Engineer designs and implements machine learning models that can run efficiently on embedded systems, such as microcontrollers and edge devices. Their work involves optimizing algorithms to fit within the resource constraints of these devices, integrating ML models into hardware, and ensuring real-time performance. They collaborate closely with hardware engineers and software developers to deploy intelligent features in products like smart sensors, IoT devices, and autonomous systems.

What are some common challenges faced by Embedded Machine Learning Engineers when deploying models to hardware devices?

One of the main challenges for Embedded Machine Learning Engineers is optimizing machine learning models to run efficiently on devices with limited memory, processing power, and energy capacity. Ensuring real-time performance while maintaining accuracy often requires model quantization, pruning, or using lightweight architectures. Additionally, engineers must carefully manage hardware-software integration and address issues like compatibility with various microcontrollers and ensuring secure, reliable updates for deployed models. Close collaboration with hardware engineers and software developers is essential to overcome these challenges and deliver robust embedded AI solutions.

What is the difference between Embedded Machine Learning Engineer vs Firmware Engineer?

AspectEmbedded Machine Learning EngineerFirmware Engineer
Required CredentialsBachelor's/Master's in Computer Science, Electrical Engineering, or related; knowledge of ML frameworksBachelor's in Electrical Engineering, Computer Engineering, or related; embedded systems experience
Work EnvironmentDevelops ML models for embedded devices, often in IoT or smart devicesDesigns and implements low-level firmware for hardware devices
Industry UsageTech companies, IoT, consumer electronics, automotiveConsumer electronics, automotive, industrial equipment

The Embedded Machine Learning Engineer focuses on integrating machine learning models into embedded systems, while the Firmware Engineer specializes in developing low-level software for hardware devices. Both roles require embedded systems knowledge but differ in their core focus and skill sets.

What are popular job titles related to Embedded Machine Learning Engineer jobs in Washington? For Embedded Machine Learning Engineer jobs in Washington, the most frequently searched job titles are:
What job categories do people searching Embedded Machine Learning Engineer jobs in Washington look for? The top searched job categories for Embedded Machine Learning Engineer jobs in Washington are:
What cities in Washington are hiring for Embedded Machine Learning Engineer jobs? Cities in Washington with the most Embedded Machine Learning Engineer job openings:
Embedded Machine Learning Engineer jobs near you

Machine Learning Engineer

10a Labs

Washington, DC • On-site

$130K - $200K/yr

Full-time

Medical, Dental, Vision, PTO

Re-posted 6 days ago

Job description

About 10a Labs: 10a Labs is the safety and threat-intelligence layer trusted by frontier AI labs, AI unicorns, Fortune 10 companies, and leading global technology platforms. Our adversarial red teaming, model evaluations, and intelligence collection enable engineering, safety, and security teams to stay ahead of evolving threats and deploy AI systems safely.
About the Role:
We are seeking a Machine Learning Engineer (3-5+ years of experience) to help design, build, evaluate, and deploy advanced machine learning systems across a range of safety, security, and intelligence applications.
This role spans the full ML lifecycle, from dataset development and experimentation to model training, evaluation, deployment, and monitoring. You will work both independently and collaboratively across projects involving multimodal classification systems, frontier model evaluations, model distillation research, and agentic workflows. The ideal candidate combines strong engineering fundamentals with a research mindset and enjoys tackling ambiguous, high-impact problems at the frontier of AI.
You will collaborate closely with researchers, software engineers, red teamers, and subject-matter experts to develop production-ready systems that support leading AI organizations and technology companies.
Responsibilities may include:
  • Design, train, evaluate, and deploy machine learning models across text, image, audio, and multimodal domains.
  • Develop and improve classification systems for safety, security, abuse detection, and intelligence applications.
  • Conduct experiments to benchmark, evaluate, and compare AI models, including large language models and multimodal systems.
  • Contribute to model distillation, optimization, and fine-tuning efforts to improve performance, efficiency, and deployability.
  • Design evaluation pipelines, metrics, and testing frameworks to measure model capabilities, reliability, and safety.
  • Build agentic systems and automated workflows for evaluation, red teaming, research, and large-scale experimentation.
  • Own ML projects from initial research and prototyping through production deployment and monitoring.
  • Partner with software engineers to productionize ML systems and support ongoing improvements.
  • Provide technical expertise and guidance across client engagements and internal research initiatives.

We're looking for someone who:
  • Brings curiosity, creativity, and rigor to ambiguous research and engineering problems, with a bias toward experimentation and rapid iteration;
  • Thrives in collaborative, interdisciplinary environments while also being comfortable independently driving projects to completion;
  • Communicates technical concepts clearly to both technical and non-technical audiences;
  • Is resourceful, proactive, and comfortable operating in a fast-moving startup environment.
  • Is excited about developing novel approaches that advance the state of AI safety, evaluation, and security.

Requirements:
  • 3-5+ years of professional experience building and deploying machine learning systems.
  • Strong proficiency in Python and modern machine learning frameworks such as PyTorch and/or TensorFlow
  • Experience working across multiple modalities, with expertise in one or more of:
    • Computer Vision: image classification, object detection, OCR, segmentation, deepfake detection, multimodal vision-language systems, or related areas.
    • Natural Language Processing: LLMs, text classification, information extraction, retrieval systems, speech-to-text, agentic applications, or related areas.
  • Experience training, fine-tuning, evaluating, and deploying machine learning models in production environments.
  • Experience designing evaluation methodologies, benchmarking systems, and model performance metrics.
  • Experience with MLOps tools and practices (Docker, Kubernetes, CI/CD for ML, MLflow, etc.)
  • Experience with cloud platforms such as Google Cloud Platform (preferred), AWS, or Azure, including ML infrastructure, workflow orchestration, storage, and database services.
  • Familiarity or experience with model distillation, synthetic data generation, reinforcement learning, or AI evaluation research is strongly preferred.

Preferred:
Experience working with frontier language models, multimodal foundation models, or AI safety evaluations.Prior experience in cybersecurity, trust and safety, abuse prevention, threat intelligence, or related domains.Experience with retrieval-augmented generation (RAG), AI agent frameworks, and context orchestration systems such as LangChain, LlamaIndex, OpenAI Agents, or AutoGen.
Compensation:
  • Salary Range: $130K-$200K, depending on experience and location
  • Bonus: Performance-based annual bonus
  • Professional Development: Support for conferences, continuing education, or leadership training
  • Work Environment: Fully remote, U.S.-based
  • Health Benefits: Comprehensive health, dental, and vision coverage

Time Off: Generous PTO and paid holiday schedule

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