1

Embedded Ai Engineer Jobs in Ohio (NOW HIRING)

Lead the transition from traditional SDLC to a mature Secure SDLC with embedded DevSecOps controls ... AI Security & AI Engineering Partnership * Act as the dedicated security partner to CBIZ's AI ...

Senior Application Security Engineer

Independence, OH ยท Hybrid

$111K - $153K/yr

Lead the transition from traditional SDLC to a mature Secure SDLC with embedded DevSecOps controls ... AI Security & AI Engineering Partnership * Act as the dedicated security partner to CBIZ's AI ...

Software Engineer II

Amherst, OH ยท On-site

$85K - $116K/yr

The Software Engineer II designs, develops, tests, and documents software for embedded systems and ... Evaluate and implement software technologies, including AI/ML and data-driven solutions, to improve ...

Software Engineer II

Amherst, OH ยท On-site

$85K - $116K/yr

The Software Engineer II designs, develops, tests, and documents software for embedded systems and ... Evaluate and implement software technologies, including AI/ML and data-driven solutions, to improve ...

Software Engineer II

Amherst, OH ยท On-site

$85K - $116K/yr

The Software Engineer II designs, develops, tests, and documents software for embedded systems and ... Evaluate and implement software technologies, including AI/ML and data-driven solutions, to improve ...

Showing results 41-60

Embedded Ai Engineer information

See Ohio salary details

$66.5K

$145.8K

$165.4K

How much do embedded ai engineer jobs pay per year?

As of Aug 18, 2026, the average yearly pay for embedded ai engineer in Ohio is $145,821.00, according to ZipRecruiter salary data. Most workers in this role earn between $125,000.00 and $164,500.00 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 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 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 popular job titles related to Embedded Ai Engineer jobs in Ohio?

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

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

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

Infographic showing various Embedded Ai Engineer job openings in Ohio as of August 2026, with employment types broken down into 81% Full Time, 17% Part Time, and 2% Contract. Highlights an 63% Physical, 4% Hybrid, and 33% Remote job distribution, with an average salary of $145,821 per year, or $70.1 per hour.

Senior Product Security Engineer

Myers and Stauffer

Independence, OH โ€ข Hybrid

Full-time

Posted 22 days ago


Job description

#LI-CR2 #LI-Hybrid

CBIZ, Inc. (NYSE: CBZ) is a leading professional services advisor to middle-market businesses nationwide. With industry knowledge and expertise in accounting, tax, advisory, benefits, insurance, and technology, CBIZ delivers actionable insights to help clients anticipate what is next and discover new ways toย accelerate growth. CBIZ has more than 9,500 team members across 23 major markets coast to coast.

CBIZ strives to be our team members' employer of choice by creating an environment where team members are appreciated, recognized for their contributions, and provided with opportunities to grow, both personally and professionally, throughout their careers.

Together, CBIZ and CBIZ CPAs are ranked as one of the top providers of accounting services in the United States. CBIZ CPAs is an independent CPA firm that provides audit, review and attest services, while CBIZ provides business consulting, tax and financial services. In certain jurisdictions, CBIZ CPAs operates under its previous name, Mayer Hoffman McCann P.C.

Minimum Qualificationsย 

  • College Degree or equivalent required
  • 8 years related experience
  • Expert technical knowledge
  • Knowledge of industry regulations
  • Ability to lead and coordinate the team activities of others
  • Ability to formulate, document and recommend new policies and procedures
  • Able to work in and lead a team
  • Demonstrated ability to communicate verbally and in writing throughout all levels of an organization, both internally and externally
  • Ability to travel as required by business and on-call availability

The Senior Product Security Engineer is a deeply technical, hands-on engineering and architect-level role responsible for establishing and leading the Product Security function at CBIZ. As the first dedicated hire in this domain, this position serves as the single point of accountability for product security across the enterprise - defining strategy, building the program from the ground up, and operating as a trusted architect and advisor to development, engineering, platform, and AI teams.

Operating within a matrix organization, the role champions a security-first mindset across business groups, embeds secure-by-design principles into the Software Development Lifecycle (SDLC), and leads the transformation to a mature Secure SDLC with a DevSecOps focus. The engineer acts as a guiding authority on secure coding, threat modeling, application architecture, AI/LLM security, and software supply chain integrity.

This role requires an experienced builder with a strong coding background, demonstrated AI security expertise, and the ability to influence without direct authority - operating as a credible technical peer to senior developers and AI engineers alike.

Essential Functions and Primary Duties

Product Security Strategy & Architecture

  • Define and own the enterprise Product Security strategy, roadmap, reference architectures, and secure design patterns for web, mobile, API, microservices, serverless, and AI-enabled applications.

  • Serve as the Product Security Architect for major initiatives, providing authoritative guidance on authentication, authorization, session management, encryption, key management, secrets handling, and API security.

  • Establish secure-by-design standards, control libraries, and engineering guardrails that scale across product lines and business units.

Secure SDLC & DevSecOps Enablement

  • Lead the transition from traditional SDLC to a mature Secure SDLC with embedded DevSecOps controls, integrating security gates into every phase including design, code, build, test, deploy, and operate.

  • Architect and operationalize security automation including SAST, DAST, SCA, container image scanning, and secrets detection.

  • Define vulnerability remediation of SLAs and drive measurable reduction in mean-time-to-remediate.

  • Build developer-friendly tooling, paved-road patterns, and self-service guardrails that enable engineering velocity without compromising security.

AI Security & AI Engineering Partnership

  • Act as the dedicated security partner to CBIZ's AI engineering team, reviewing AI/ML configurations, agent designs, model integrations, and deployment patterns to ensure they meet enterprise security and privacy standards.

  • Establish AI security best practices and guardrails for generative AI, agentic workflows, RAG pipelines, and LLM-powered applications, aligned to the OWASP Top 10 for LLM Applications including prompt injection, insecure output handling, training data poisoning, supply chain vulnerabilities, sensitive information disclosure, excessive agency, and model theft.

  • Review and harden AI model configurations, system prompts, tool and function calling permissions, content filters, rate limits, and identity boundaries for agents operating against enterprise data.

  • Establish controls for AI-generated code review to ensure AI-assisted development does not bypass secure SDLC checkpoints.

  • Define data protection and access controls for AI workloads including grounding data governance, vector database security, and PII handling prompts and responses.

  • Partner with AI engineers on model risk management, red-teaming, and adversarial testing.

  • Stay current with the evolving AI regulatory landscape (NIST AI RMF, EU AI Act, ISO/IEC 42001) and translate requirements into engineering controls.

Threat Modeling & Secure Design Reviews

  • Facilitate threat modeling sessions using STRIDE, PASTA, and MITRE ATLAS for AI/ML systems producing actionable mitigations and ranked risk registers.

  • Conduct architecture and design reviews to identify weaknesses before code is written, partnering with solution architects and engineering leads.

Code Review & Vulnerability Management

  • Perform manual and tool-assisted secure code reviews against OWASP Top 10, CWE Top 25, and SANS 25, providing remediation guidance with corrected code where appropriate.

  • Triage scanner findings and own application vulnerability management workflows, SLA tracking, and executive reporting on AppSec posture.

Software Supply Chain Security

  • Define and enforce controls for third-party and open-source components, dependency hygiene, SBOM generation, and signed artifacts, including AI model provenance and dataset integrity.

  • Harden source repositories, build systems, and deployment environments against supply chain compromise.

Matrix Leadership & Security Mindset Advocacy

  • Navigate CBIZ's matrix organization to influence development, engineering, AI, platform, and product teams.

  • Act as the visible, accessible point of contact for application security, embedding into engineering rituals such as design reviews, architecture councils, and sprint planning.

  • Lead developer enablement programs including secure coding training, threat modeling workshops, a security champions network, and lunch-and-learn sessions across business groups.

Incident Response & Executive Reporting

  • Serve as the AppSec and AI security subject matter expert during incident response, escalations, and post-incident reviews.

  • Produce board-ready and executive-level reporting on AppSec maturity, AI security posture, key risk indicators, and program outcomes.

Preferred Qualifications

  • 8+ years of progressive experience in software engineering, application development, or platform engineering, with at least 4 years focused on product security, DevSecOps, or security architecture.

  • Mandatory hands-on coding background with proficiency in one or more modern languages such as Python, Java, C#/.NET, JavaScript/TypeScript, or Go, and the demonstrated ability to read, write, and review production code as a peer to senior developers.

  • Mandatory experience working directly with development, engineering, and AI/ML teams within a matrix environment.

  • Mandatory hands-on AI security experience, including reviewing AI/ML system architectures, securing LLM integrations, evaluating model configurations, and applying frameworks such as OWASP Top 10 for LLMs, MITRE ATLAS, and the NIST AI Risk Management Framework.

  • Deep expertise in Secure SDLC, OWASP Top 10, CWE Top 25, MITRE ATT&CK, and CVSS.

  • Hands-on experience with AppSec tooling such as SAST (Semgrep, CodeQL, SonarQube, Checkmarx, Veracode), DAST (Burp Suite, OWASP ZAP), SCA (Snyk, Black Duck), IaC scanning, and secrets detection.

  • Strong understanding of CI/CD platforms including GitHub Actions, GitLab CI, Azure DevOps, and Jenkins, with experience hardening pipeline security.

  • Cloud security expertise across Microsoft Azure and AWS, including IAM, container security (Kubernetes), workload protection, and CNAPP platforms.

  • Familiarity with API security (REST, GraphQL), authentication and authorization standards (OAuth 2.0, OIDC, SAML), and modern cryptography.

  • Demonstrated ability to influence without authority and navigate a matrix organization across multiple business groups.