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Embedded Ai Engineer Jobs in Virginia (NOW HIRING)

They are seeking a Senior AI Engineer to build advanced AI-driven systems for vulnerability ... embedded systems, mobile platforms, or cloud-native workloads. • Publications, conference ...

Senior AI/ML Engineer

Herndon, VA · On-site

$107K - $147K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Senior AI/ML Engineer Location: Herndon, VA (Hybrid Work) Preferred: ship Node.Digital is an ... You will focus on building generative AI applications with embedded artifcial intelligence or ...

Senior AI/ML Engineer

Herndon, VA · On-site +1

$107K - $147K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Senior AI/ML Engineer Location: Herndon, VA (Hybrid Work) Preferred: US Citizenship Node.Digital is ... You will focus on building generative AI applications with embedded artifcial intelligence or ...

Senior AI/ML Engineer

Herndon, VA · On-site

$140 - $190/hr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

You will focus on building generative AI applications with embedded artifcial intelligence or ... UiPath RPA Developer Certification and UiPath AI Center Experience * Knowledge of chatbot ...

Senior AI/ML Engineer

Herndon, VA · On-site

$107K - $147K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Senior AI/ML Engineer Location: Herndon, VA (Hybrid Work) Preferred: US Citizenship Node.Digital is ... You will focus on building generative AI applications with embedded artifcial intelligence or ...

Embedded Software Engineer

King George, VA · On-site

$69K - $158K/yr

  • Medical

  • Life

  • Retirement

  • PTO

Embedded Software Engineer The Opportunity: As an embedded software engineer, you know how to ... Candidate AI Usage Policy AI is a part of our daily work at Booz Allen, and we are committed to the ...

Senior AI/ML Engineer

Herndon, VA · On-site

$107K - $147K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Senior AI/ML Engineer Location: Herndon, VA (Hybrid Work) Preferred: US Citizenship Node.Digital is ... You will focus on building generative AI applications with embedded artifcial intelligence or ...

$69K - $158K/yr

  • Medical

  • Life

  • Retirement

  • PTO

Embedded Software Engineer The Opportunity: As an embedded software engineer, you know how to ... Candidate AI Usage Policy AI is a part of our daily work at Booz Allen, and we are committed to the ...

AI Engineer III - Blue Ring

Reston, VA

$59.75 - $80.25/hr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

We are seeking an AI Engineer III to join the Blue Ring software team ... You will help develop and deliver the AI and machine learning capabilities embedded in Blue Ring ...

Showing results 21-40

Embedded Ai Engineer information

See Virginia salary details

$69.4K

$152.1K

$172.5K

How much do embedded ai engineer jobs pay per year?

As of Aug 16, 2026, the average yearly pay for embedded ai engineer in Virginia is $152,068.00, according to ZipRecruiter salary data. Most workers in this role earn between $130,400.00 and $171,500.00 per year, depending on experience, location, and employer.

What is an embedded AI engineer?

An Embedded AI Engineer is a professional who designs, develops, and implements artificial intelligence (AI) algorithms and models directly onto embedded systems, such as microcontrollers or edge devices. Their work involves optimizing AI solutions to run efficiently on hardware with limited computing resources, power, and memory. They collaborate with hardware engineers and software developers to integrate machine learning, computer vision, or other AI functionalities into products like smart appliances, autonomous vehicles, or IoT devices. Their expertise helps bring intelligent features directly to devices, enabling real-time decision-making without needing constant cloud connectivity.

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

CriteriaEmbedded Ai EngineerMachine Learning Engineer
Required CredentialsBachelor's in Electrical Engineering, Computer Science, or related; knowledge of embedded systemsBachelor's or Master's in Computer Science, Data Science, or related; strong programming skills
Work EnvironmentEmbedded systems, IoT devices, hardware integrationData centers, cloud platforms, software development environments
Employer & Industry UsageConsumer electronics, automotive, IoT companiesTech firms, startups, research institutions
Common Search & ComparisonYesNo

Embedded Ai Engineers focus on integrating AI algorithms into embedded hardware and IoT devices, requiring knowledge of hardware constraints and embedded programming. Machine Learning Engineers develop models primarily for software applications and data analysis. While both roles involve AI, Embedded Ai Engineers specialize in hardware-software integration within embedded systems, whereas Machine Learning Engineers work on developing and deploying AI models in software environments.

What are the key skills and qualifications needed to thrive as an embedded AI engineer?

To thrive as an Embedded AI Engineer, you need expertise in embedded systems, AI/ML algorithms, programming languages like C/C++ and Python, and typically a degree in computer engineering or a related field. Familiarity with development tools such as TensorFlow Lite, ONNX, embedded Linux, and microcontroller platforms is essential, along with experience deploying AI models on resource-constrained devices. Strong problem-solving, collaboration, and communication skills help you work effectively in multidisciplinary teams and address real-world challenges. These skills ensure efficient integration of AI into embedded systems, enabling innovative, high-performance solutions for edge computing.

How does an embedded AI engineer typically collaborate with hardware and software teams during a project?

Embedded AI Engineers work closely with both hardware and software teams to ensure AI models are efficiently integrated into resource-constrained devices. They often collaborate with hardware engineers to optimize model performance based on device limitations like memory and processing power. At the same time, they coordinate with software developers to design efficient firmware and manage data pipelines. Regular cross-functional meetings and code reviews are common to address integration challenges and maintain alignment throughout the project lifecycle.

What are popular job titles related to Embedded Ai Engineer jobs in Virginia?

For Embedded Ai Engineer jobs in Virginia, the most frequently searched job titles are:

What cities in Virginia are hiring for Embedded Ai Engineer jobs?

Cities in Virginia with the most Embedded Ai Engineer job openings:

Infographic showing various Embedded Ai Engineer job openings in Virginia as of August 2026, with employment types broken down into 73% Full Time, and 27% Contract. Highlights an 100% In-person job distribution, with an average salary of $152,068 per year, or $73.1 per hour.

EMERGING TECHNOLOGY / CYBERSECURITY ENGINEER

Hiring Our Heroes

Arlington, VA

Full-time

Re-posted 24 days ago


Job description

EMERGING TECHNOLOGY / CYBERSECURITY ENGINEER

MILITARY FRIENDLY & PREFERRED - HOH SPONSOR

Position Overview

The Emerging Technology / Cybersecurity Engineer will support Zermount and our federal client in modernizing cybersecurity authorization, cloud security, security architecture review, and emerging technology assessment processes.

This is a senior, client-facing cybersecurity engineering and assessment role focused on helping the client securely adopt SaaS, AI-enabled technologies, cloud services, and other emerging capabilities. The position blends federal RMF and continuous ATO expertise, security architecture review, AI security testing, cloud compliance, control validation, and process modernization.

The successful candidate will help reduce authorization timelines by developing reusable security patterns, repeatable assessment baselines, structured test plans, and continuous ATO-ready evidence. The ideal candidate has strong experience in federal cybersecurity, RMF, ATO, cloud security, security architecture, SaaS/product assessments, vulnerability management, emerging technology risk, and AI security

This position requires a hands-on professional who can work across cybersecurity, engineering, procurement, legal, privacy, records management, vendor, product, and mission teams to evaluate technologies, validate controls, document risk, and accelerate secure adoption.

Duties & Responsibilities

Security Architecture Review

  • Conduct Security Architecture Reviews for SaaS, cloud, AI-enabled tools, non-COTS technologies, embedded AI capabilities, operating system baselines, and supporting infrastructure.
  • Integrate architecture review activities with existing cybersecurity workflows, including ATO intake, security assessment, vulnerability scanning, cloud compliance, change management, and authorization decision support.
  • Review system designs, data flows, identity models, access controls, logging approaches, network architecture, tenant isolation, administrative control structures, and security boundary assumptions.
  • Develop security architecture patterns and reusable designs that enable faster assessments and ATO decisions by aligning solutions with federal security controls early in the lifecycle.
  • Translate technical architecture findings into actionable risk statements, control recommendations, remediation plans, POA&Ms, and acceptance decision inputs.

Emerging Technology Assessment and AI Security Testing

  • Perform cybersecurity assessments of commercial SaaS products, non-COTS AI products, embedded AI components, cloud-hosted services, operating system baselines, and related technologies during intake and change events.
  • Evaluate AI-specific threats and vulnerabilities, to include direct, indirect, and instruction smuggling prompt injection, jailbreak susceptibility, data leakage/sensitive data exposure, model poisoning, RAG/vector database exposure, unintended model behavior, tool or agent misuse, insecure plugin use, and unsafe browsing capabilities.
  • Execute structured AI and emerging technology testing, including functional and accuracy testing, adversarial testing, data exfiltration probes, red-team scenarios, control regression testing, and validation of previously accepted security controls.
  • Develop structured AI test cases, adversarial prompts, expected results, pass/fail criteria, scoring rubrics, and repeatable evaluation scripts aligned to NIST AI RMF, OWASP Top 10 for LLM/GenAI, MITRE ATLAS, client security baselines, and federal ATO acceptance criteria.
  • Validate logging coverage, DLP efficacy, safety controls, accuracy thresholds, telemetry availability, and technical acceptance criteria prior to production use.
  • Document findings, support remediation planning, and perform retesting to verify closure of identified security gaps.

Engineering Controls and Implementation Support

  • Design and support implementation of baseline security controls, including identity integration, RBAC, DLP, logging and telemetry, SIEM integration, network segmentation, tenant isolation, and feature-level security controls.
  • Develop or contribute automation artifacts such as scripts, Infrastructure as Code snippets, configuration templates, security checklists, and deployment patterns.
  • Configure, test, or validate vendor systems and internal deployments against approved baseline controls and client security requirements.
  • Coordinate with commercial vendors, cloud teams, platform teams, product teams, and internal engineering teams to implement, test, and validate required security controls.
  • Provide recommendations to disable or restrict high-risk capabilities such as public browsing, external plugins, unmanaged connectors, excessive permissions, unapproved data access, or other features that increase mission or data risk.

Process, Baseline, and Knowledge Transfer

  • Develop repeatable test plans, assessment checklists, intake procedures, triage workflows, pilot assessment templates, production readiness criteria, and operational runbooks.
  • Create scalable baseline templates for Low, Moderate, and High-risk tiers with required controls, evidence expectations, test procedures, and acceptance criteria.
  • Support development of reusable security patterns for SaaS, AI-enabled applications, cloud-hosted services, operating system baselines, and emerging technology deployments.
  • Deliver knowledge transfer, training, and working sessions for client staff to operate and maintain security baselines, assessment processes, and test suites.
  • Support continuous improvement of cybersecurity intake, assessment, authorization, monitoring, and remediation processes.

Product and Stakeholder Support

  • Engage with commercial vendors, procurement, legal, engineering, cybersecurity, privacy, records management, product, and mission teams to support technology intake, contracting, evidence collection, control validation, and remediation activities.
  • Coordinate with vendor contacts and internal client stakeholders to obtain security documentation, clarify technical capabilities, validate control implementation, and resolve assessment findings.
  • Provide regular status reporting, metrics, risk updates, testing results, and recommendations to support timely security acceptance and authorization decisions.
  • Track assessment activities, findings, remediation actions, POA&Ms, retesting results, risk decisions, and authorization milestones through established client workflows.
  • Participate in client meetings, technical working sessions, vendor discussions, security reviews, and authorization decision support activities.
  • Prepare executive-level status reports, technical assessment summaries, testing reports, briefings, and recommendations to mitigate identified risks.
  • Perform additional duties as required.

Roadmap, Strategy, and Continuous ATO Support

  • Provide Support the development and execution of cybersecurity authorization and compliance strategies designed to reduce ATO processing time and improve cloud compliance.
  • Provide input into continuous ATO and continuous monitoring approaches, including evidence collection, automated control validation, risk triggers, POA&M visibility, and decision-ready reporting.
  • Assist the Cybersecurity Division in designing compliance processes and systems that support continuous authorization and reduce manual processing delays.
  • Plan and run controlled pilots using synthetic or de-identified data, where feasible, to validate technical, security, privacy, and operational readiness.
  • Support development of roadmap recommendations, implementation priorities, metrics, and process improvements to advance secure adoption of SaaS, AI-enabled technologies, cloud services, and emerging capabilities.

Qualifications

Minimum Requirements:

  • 5+ years of cybersecurity, security architecture, cloud security, GRC, RMF, ATO, or federal compliance experience.
  • Experience supporting federal cybersecurity programs, including RMF, ATO, security assessment, continuous monitoring, vulnerability management, or cloud compliance.
  • Working knowledge of the NIST Risk Management Framework, NIST Cybersecurity Framework, NIST Special Publications, FISMA, FedRAMP, and federal security control expectations.
  • Experience assessing SaaS, cloud services, commercial products, emerging technologies, or enterprise systems for security and compliance risk.
  • Ability to review technical architectures, data flows, identity models, access controls, logging, network segmentation, tenant isolation, and security control implementation.
  • Experience developing POA&Ms, risk statements, security recommendations, assessment reports, test plans, metrics, and executive-level status updates.
  • Strong written and verbal communication skills, including the ability to brief technical risks and recommendations to cybersecurity, engineering, program, and executive stakeholders.
  • Ability to coordinate across vendors, engineering teams, cybersecurity teams, procurement, legal, privacy, records management, and mission stakeholders.

Preferred Qualifications:

  • Experience assessing AI-enabled technologies, machine learning platforms, generative AI tools, RAG architectures, vector databases, AI agents, or commercial AI SaaS products.
  • Knowledge of AI security risks, including prompt injection, jailbreaks, model misuse, data leakage, training data exposure, adversarial testing, tool/agent misuse, and AI governance.
  • Experience with continuous ATO, ongoing authorization, automated evidence collection, cybersecurity authorization modernization, or continuous monitoring.
  • Experience with cloud environments such as AWS, Azure, or Google Cloud Platform.
  • Experience with DevSecOps, CI/CD security, Infrastructure as Code, container security, or cloud-native security controls.
  • Experience integrating security telemetry into SIEM, SOAR, GRC, vulnerability management, or continuous monitoring platforms.
  • Familiarity with security tools such as Splunk, Microsoft Sentinel, QRadar, Tenable, Security Hub, Defender, Prisma Cloud, ServiceNow, or similar platforms.
  • Experience with AI/LLM evaluation, red-teaming, guardrail, or model-security testing tools such as Microsoft PyRIT, NVIDIA Garak, Promptfoo, DeepEval, OpenAI Evals, Azure AI Foundry Evaluation, Amazon Bedrock Guardrails, Google Vertex AI Evaluation, Lakera Guard, HiddenLayer, Protect AI ModelScan/Guardian, or equivalent tools.
  • Experience developing reusable security baselines, control templates, architecture patterns, runbooks, assessment playbooks, and technical acceptance criteria.

Competencies

  • Security architecture review
  • AI and emerging technology risk assessment
  • Cloud and SaaS security assessment
  • Continuous ATO and compliance modernization
  • Control validation and evidence collection
  • Federal cybersecurity and RMF expertise
  • POA&M development and remediation tracking
  • Security testing and adversarial assessment
  • Vendor and stakeholder coordination
  • Executive-level reporting and recommendations
  • Process development, baselining, and knowledge transfer

Education

  • Bachelor of Science (or higher) in one of the following: Computer Science, Information Technology, Cybersecurity, Engineering, or equivalent.
    • Years of experience may be considered in lieu of a degree.

Certifications

At least one of the following certifications is required:

  • GIAC Certified Incident Handler (GCIH); Security+; Certified Information Security Manager (CISM), Certified in Governance of Enterprise IT (CGEIT); Certified Information Systems Security Professional (CISSP); Certified Information Security Auditor (CISA); Certified Cloud Security Professional (CCSP); AWS Certified Security Specialist; Microsoft Certified: Cybersecurity Architect Expert; Microsoft Azure Azure Security Engineer Associate; or another equal GIAC certification related to cloud, incident response, security engineering, or penetration testing.

Clearance Level

  • Public Trust, but an active Secret Clearance is preferred.

Work Location

  • Primary location(s) are Arlington and Alexandria VA. Remote work is authorized, but the employee may have to report to one of the primary sites occasionally or as requested by management or the client.

Hours of Operation

  • 6:00 am ET – 6:00 pm ET