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Embedded Machine Learning Jobs in Washington (NOW HIRING)

Mixpeek

Machine Learning Engineer

Washington, DC · On-site

$180 - $240/hr

The Machine Forward Deployed Learning Engineer position requires a mix of software development, LLM ... Experience in a customer-facing or embedded delivery role. * Exposure to federated or privacy ...

Expression

Data Engineer

Arlington, VA · On-site

$131K - $158K/yr

Develop and deploy standalone and embedded machine learning models supporting mission decision-making, automation, anomaly detection, and pattern recognition. * Select and implement appropriate ...

Expression Networks

Data Engineer

Arlington, VA · On-site

$131K - $158K/yr

Develop and deploy standalone and embedded machine learning models supporting mission decision-making, automation, anomaly detection, and pattern recognition. * Select and implement appropriate ...

Hatch IT

Data Engineer

Washington, DC · On-site

$129K - $155K/yr

Develop and deploy standalone and embedded machine learning models supporting mission decision-making, automation, anomaly detection, and pattern recognition. * Select and implement appropriate ...

Expression

Data Engineer

Arlington, VA

$131K - $158K/yr

Develop and deploy standalone and embedded machine learning models supporting mission decision-making, automation, anomaly detection, and pattern recognition. * Select and implement appropriate ...

Hatch IT

Data Engineer

Washington, DC

$129K - $155K/yr

Develop and deploy standalone and embedded machine learning models supporting mission decision-making, automation, anomaly detection, and pattern recognition. * Select and implement appropriate ...

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Embedded Machine Learning information

What is an embedded machine learning?

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 embedded machine learning?

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.

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 August 2026, with employment types broken down into 6% Internship, and 94% Full Time. Highlights an 91% In-person, and 9% Hybrid job distribution.

Machine Learning Engineer

Washington, DC • On-site

Mixpeek
See more jobs at Mixpeekexternal

$180 - $240/hr

Other

Medical, Retirement, PTO

Posted 8 days ago

Job description

About Adelphi

Adelphi's Vision is to build the biggest AI Deployment company for National Security, which requires talent that is extremely good at problem solving, gets excited about National Security challenges, and thrives in software factories. We're executing a Mission that (1) Identifies the core bottlenecks in Warfighting, Intelligence and the Enterprise, (2) Assigns top talent with backgrounds in systems and machine learning engineering, and (3) Finds repeatability in solution development to target similar offices across the DoW/IC. The business has 10x'd in under two years and has built trust with the senior most leaders at the Department of War and Intelligence Community.

About the Role:

The Machine Forward Deployed Learning Engineer position requires a mix of software development, LLM Ops, and SecDevOps practices, resulting in an exciting, fast-paced engineering role. This role requires the ability to contribute to solutions across the full LLM stack, from the OS, storage, and network up to the API and transport layer. Experience in the defense or intelligence fields is required. You will contribute to end-to-end delivery of agentic, full-stack systems built on top of frontier models, from first prototype to stable production, embedded alongside our defense and intelligence customers.

Location:

This role is based in the DC/Metro area; remote candidates will be considered with 25% travel expected.

Clearance Requirement:

An active U.S. Government clearance is strongly preferred, but we are open to clearance eligible candidates.

Is this you?

We are an AI-native engineering team. We expect every engineer to leverage LLMs and AI tooling as a core part of how they design, build, ship, and operate agentic systems that turn frontier-model capability into mission outcomes.

  • You use AI coding tools (Claude Code, Cursor, Copilot) daily and instinctively.

  • You leverage LLMs across the development lifecycle, and stay current with emerging models and tooling.

  • You have working knowledge of modern agent frameworks and SDKs (LangGraph, OpenAI Agents SDK, Claude Agent SDK, AutoGen, or similar).

  • Familiarity with MCP or similar LLM integration frameworks.

  • You have a clear-eyed view of AI limitations. You know when to trust AI-generated output and when to verify.

Expectations:

  • Contribute to end-to-end delivery of agentic, full-stack systems from prototype to production, embedded alongside defense and intelligence customers.

  • Build and deploy ML services leveraging LLMs, embeddings, RAG, and agent orchestration into production environments, including classified and air-gapped ones.

  • Work directly with customers to understand problems, support delivery sequencing, and ship AI applications under real-world constraints.

  • Help codify repeatable patterns into reusable tools and building blocks that help the team ship faster.

Bonus Points:

  • Familiarity with infrastructure management (Docker, Kubernetes, AWS).

  • Exposure to encryption, authentication, Linux systems administration, DevOps, or SRE.

  • Any production experience with agentic services or forward-deployed AI applications.

  • Experience in a customer-facing or embedded delivery role.

  • Exposure to federated or privacy-preserving data architectures.

Benefits:

  • Healthcare coverage: 100% employee premium and 50% dependents premium coverage of a platinum-level plan.

  • 401K with 2% company match.

  • Equity in a seed-stage business.

  • Access to 6713 Club - a members-only club in McLean, VA.

  • $500 monthly Physical and Mental Health reimbursement program.

  • Flexible PTO policy.

  • Competitive salary and equity compensation.

  • Opportunity to work on impactful projects in the national security sector.

  • Career growth and leadership opportunities in a dynamic, innovative environment.

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