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Embedded Machine Learning Engineer Jobs in Sandston, VA

WorkNovas LLC

Machine Learning Engineer

Richmond, VA · On-site

Machine Learning Engineer Richmond, Virginia (5 Days Onsite) need local within commute About the Role We are seeking a Machine Learning Engineer with expertise in agentic AI systems to design, build ...

Bespoke Labs

Machine Learning Engineer

Richmond, VA · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Capital One

Lead Machine Learning Engineer

Richmond, VA · On-site

$101K - $133K/yr

Lead Machine Learning Engineer At Capital One, we are changing banking for good by creating responsible and reliable AI-powered systems. Our investments in technology infrastructure and world-class ...

Capital One

Lead Machine Learning Engineer

Richmond, VA

$101K - $133K/yr

Lead Machine Learning Engineer At Capital One, we are changing banking for good by creating responsible and reliable AI-powered systems. Our investments in technology infrastructure and world-class ...

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All JobsEmbedded Machine Learning Engineer JobsEmbedded Machine Learning Engineer Jobs in Sandston, VA

Embedded Machine Learning Engineer information

See Sandston, VA salary details

$68.5K

$150.1K

$170.3K

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

As of Jul 8, 2026, the average yearly pay for embedded machine learning engineer in Sandston, VA is $150,103.00, according to ZipRecruiter salary data. Most workers in this role earn between $128,700.00 and $169,300.00 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 cities near Sandston, VA are hiring for Embedded Machine Learning Engineer jobs? Cities near Sandston, VA with the most Embedded Machine Learning Engineer job openings:
Embedded Machine Learning Engineer jobs near you

Machine Learning Engineer

WorkNovas LLC

Richmond, VA • On-site

Contractor

Re-posted 16 days ago

Job description

Machine Learning Engineer  

Richmond, Virginia (5 Days Onsite) need local within commute

About the Role
We are seeking a Machine Learning Engineer with expertise in agentic AI systems to design, build, and deploy next-generation AI solutions. In this role, you will work at the intersection of LLMs, autonomous agents, retrieval-augmented generation (RAG), and enterprise-scale systems, leveraging Azure AI Foundry, Copilot Studio, and modern orchestration frameworks.
You will collaborate closely with product managers, architects, and application teams to deliver intelligent, production-grade AI agents that integrate seamlessly with business workflows and enterprise data.
Key Responsibilities
Design and implement agentic AI systems capable of planning, tool use, memory, and multi-step reasoning
Build and deploy AI solutions using Azure AI Foundry and Copilot Studio
Develop RAG pipelines integrating structured and unstructured enterprise data
Implement and optimize vector databases for semantic search and long-term agent memory
Orchestrate LLM-based agents using frameworks such as LangChain (or equivalent)
Develop scalable backend services and APIs using Python
Integrate AI agents with enterprise tools, APIs, and workflows
Evaluate, monitor, and optimize agent performance, reliability, and cost
Apply responsible AI principles including security, privacy, and governance
Stay current with advancements in LLMs, agent architectures, and Azure AI services
Required Qualifications
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
5+ years of experience in machine learning, AI engineering, or applied ML
Strong proficiency in Python for ML and backend development
Hands-on experience building LLM-based applications
Practical experience with agentic AI patterns (tool calling, planning, memory, reflection)
Experience with LangChain or similar agent orchestration frameworks
Solid understanding of RAG architectures
Experience with vector databases (e.g., Azure AI Search, Pinecone, etc.)
Familiarity with Azure cloud services and enterprise-grade deployments
Hands-on experience with MCP and/or A2A agent communication frameworks
Preferred Qualifications
Direct experience with Azure AI Foundry and Copilot Studio
Experience integrating AI agents into enterprise workflows or SaaS platforms
Knowledge of prompt engineering, evaluation frameworks, and guardrails
Experience with CI/CD, MLOps, or AI observability
Understanding of security, identity, and compliance in enterprise AI systems
Nice-to-Have
Contributions to AI prototypes, internal platforms, or open-source projects
Experience moving AI solutions from prototype to production
Strong communication skills and ability to explain complex AI systems to non-experts