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Embedded Ai Engineer Jobs in Seattle, WA (NOW HIRING)

Saransh Inc

Senior AI Engineer - Privacy

Bellevue, WA · On-site

$117K - $162K/yr

Embedded within the Data & Intelligence organization's Privacy practice, this engineer will apply ... AI Agent & LLM Engineering • Design and build multi-agent systems, orchestration layers, and ...

Workday

Principal AI Engineer

Seattle, WA

... engineers, and full-stack builders to create intelligent agents used by millions of people every ... This is production-grade AI-deeply embedded into Workday's platform-not research experiments or ...

Merican

Senior AI Engineer - Privacy

Bellevue, WA · On-site

$117K - $162K/yr

Embedded within the Data & Intelligence organization's Privacy practice, this engineer will apply ... AI Agent & LLM Engineering * Design and build multi-agent systems, orchestration layers, and ...

Salesforce, Inc.

Principal AI Engineer

Seattle, WA · On-site

Software Engineering Principal AI Engineer This is a hands-on Software Engineering Principal/Architect role embedded directly inside the Salesforce Marketing organization. You will act as the ...

Vale Concepts

Embedded Systems Engineer

Seattle, WA · On-site

$118K - $155K/yr

The ideal candidate has strong embedded systems fundamentals, experience with Rust on ... We also expect every engineer on the team to embrace modern AI-assisted software development ...

Vale Concepts

Embedded Systems Engineer

Seattle, WA · On-site

$118K - $155K/yr

The ideal candidate has strong embedded systems fundamentals, experience with Rust on ... We also expect every engineer on the team to embrace modern AI-assisted software development ...

Workday

Principal AI Engineer

Seattle, WA

... engineers, and full-stack builders to create intelligent agents used by millions of people every ... This is production-grade AI-deeply embedded into Workday's platform-not research experiments or ...

Workday

Senior/Principal AI Engineer

Seattle, WA

$142K - $196K/yr

... engineers, and full-stack builders to create intelligent agents used by millions of people every ... This is production-grade AI-deeply embedded into Workday's platform-not research experiments or ...

Workday, Inc.

Principal AI Engineer

Seattle, WA · On-site

... engineers, and full-stack builders to create intelligent agents used by millions of people every ... This is production-grade AI-deeply embedded into Workday's platform-not research experiments or ...

ARM

Senior AI Engineer

Seattle, WA · On-site

$170K - $231K/yr

Arm is building the next generation of AI platforms for developers to build Cloud to Car and Cloud ... Hands-on experience with CI/CT solutions for Embedded Systems (e.g. SiL / HiL) and monitoring tools ...

ARM

Senior AI Engineer

Seattle, WA · Hybrid

$170K - $231K/yr

Arm is building the next generation of AI platforms for developers to build Cloud to Car and Cloud ... Hands-on experience with CI/CT solutions for Embedded Systems (e.g. SiL / HiL) and monitoring tools ...

Blue Origin

AI Engineer III - Blue Ring

Seattle, WA · On-site

$65.50 - $88/hr

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 ...

INSPYR Solutions

Embedded Software Engineer

Redmond, WA · On-site

$70 - $80/hr

Title: Embedded Software Engineer Location: Redmond, WA (onsite) Duration: 6 month contract ... AI and Automated Employment Decision Tool Policy: . By submitting an application, you are ...

NRG Energy

Sr Software Engineer, Embedded

Seattle, WA · On-site

$150K - $180K/yr

Senior Embedded Software Engineer Who Are We: Vivint Smart Home is the leading provider of smart ... Experience leveraging generative AI developer tools (e.g., Claude Code, GitHub Copilot) to design ...

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All JobsEmbedded Ai Engineer JobsEmbedded Ai Engineer Jobs in Seattle, WA

Embedded Ai Engineer information

See Seattle, WA salary details

$79.7K

$174.7K

$198.1K

How much do embedded ai engineer jobs pay per year?

As of Jul 28, 2026, the average yearly pay for embedded ai engineer in Seattle, WA is $174,653.00, according to ZipRecruiter salary data. Most workers in this role earn between $149,700.00 and $197,000.00 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, and why are they important?

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 Seattle, WA? For Embedded Ai Engineer jobs in Seattle, WA, the most frequently searched job titles are:
What job categories do people searching Embedded Ai Engineer jobs in Seattle, WA look for? The top searched job categories for Embedded Ai Engineer jobs in Seattle, WA are:
What cities near Seattle, WA are hiring for Embedded Ai Engineer jobs? Cities near Seattle, WA with the most Embedded Ai Engineer job openings:
Embedded Ai Engineer jobs near you
Infographic showing various Embedded Ai Engineer job openings in Seattle, WA as of July 2026, with employment types broken down into 74% Full Time, 22% Part Time, and 4% Contract. Highlights an 71% Physical, 3% Hybrid, and 26% Remote job distribution, with an average salary of $174,653 per year, or $84 per hour.

Senior AI Engineer - Privacy

Saransh Inc

Bellevue, WA • On-site

$117K - $162K/yr

Contractor

Posted 26 days ago

Job description

Role: Senior AI Engineer – Privacy
Location: Bellevue, WA (Onsite from Day 1)
Job Type: Contract
 
Must Have Skills:
  • 7 yrs of exp – AI Engineer – Privacy
  • 7 yrs of exp – Azure Data Factory, Azure , GitLab
  • 5 yrs of exp – Databricks, Snowflake
Description:
  • The Senior AI Engineer – Privacy will design, build, and operationalize AI and agentic systems that power Client data privacy platform at scale.
  • Embedded within the Data & Intelligence organization's Privacy practice, this engineer will apply large language models (LLMs), retrieval-augmented generation (RAG), multi-agent orchestration, and foundation model capabilities to automate, enhance, and scale privacy operations — including Data Subject Request (DSR) processing, consent management, regulatory compliance monitoring, and privacy impact assessment workflows — across a customer base of over 100 million.

AI Agent & LLM Engineering
• Design and build multi-agent systems, orchestration layers, and agentic workflows using frameworks such as LangChain, LangGraph, Google ADK, or equivalent.
• Develop and operationalize RAG (Retrieval-Augmented Generation) pipelines integrating LLMs (e.g. Claude, Gemini, GPT-4) into production privacy applications.
• Implement structured prompting, decision workflows, and tool orchestration — including MCP (Model Context Protocol)-based architectures — for autonomous agent systems.
• Build AI-powered automation for privacy operations including intelligent DSR routing, threshold monitoring, agentic data quality checks, and automated regulatory notifications.
• Enable human-in-the-loop controls and escalation paths for AI-assisted decisions in sensitive privacy workflows.
Data & ML Engineering
• Build and optimize data pipelines using Azure Data Factory, Databricks, Snowflake, or PySpark to support AI model training, fine-tuning, and inference.
• Apply prompt engineering, few-shot learning, and fine-tuning techniques to adapt foundation models for privacy-specific use cases.
• Implement vector databases and embedding strategies to power RAG pipelines over Client internal privacy knowledge bases and policy documents.
• Ensure data quality, lineage, and governance standards are maintained across all AI training and inference pipelines.
Cloud & MLOps
• Deploy and manage AI workloads on Azure or AWS, including serverless inference endpoints, container registries, and GPU/compute resources.
• Build and maintain CI/CD pipelines for AI model deployment using GitLab or Azure DevOps, applying MLOps best practices.
• Implement monitoring, alerting, and performance tracking for production AI models and agent systems using Splunk, AppDynamics, or Grafana.
• Apply containerization (Docker) and orchestration (Kubernetes) to ensure scalable and reliable AI service deployments.
Responsible AI & Compliance
• Implement responsible AI principles — including fairness, transparency, and explainability — across all AI systems used in privacy operations.
• Ensure AI-assisted workflows comply with CCPA, CPRA, TCPA, and other applicable state and federal privacy regulations.
• Design and maintain audit trails and human-in-the-loop checkpoints for AI decisions affecting consumer privacy rights.
• Collaborate with legal, compliance, and privacy operations teams to translate regulatory requirements into AI solution guardrails and constraints.
Technical Leadership & Collaboration
• Partner with data engineers, full stack engineers, product managers, and privacy stakeholders to deliver end-to-end AI-powered privacy solutions.
• Mentor junior engineers on AI/ML engineering practices, agentic patterns, and responsible AI design principles.
• Produce clear technical documentation, architecture diagrams, and model cards for AI systems in production.
• Contribute to internal accelerators, reusable AI component libraries, and the broader engineering community of practice.