Application Engineer Lead, Supplier Quality Platforms
Kirkland, WA, USA +1 · Full-time · Posted Oct 22, 2026
- Location
- Kirkland, WA, USA
Sunnyvale, CA, USA - Experience
- 8 years in programming languages; 8 years in enterprise application development and data engineering; 5 years in architecture leadership, third-party integration, and engineering mentorship.
- Education
- Bachelor's degree in Computer Science, Management Information Systems, a related technical field, or equivalent practical experience.
About the job
Leads architecture and full-stack development of a supplier quality and risk intelligence platform that integrates manufacturing data with enterprise services and supports predictive AI.
What you'll do
- Architect, build, and deploy scalable enterprise applications, partner portals, APIs, and microservices.
- Create data foundations, validation, and feature pipelines for predictive machine learning and downstream AI agents.
- Provide technical direction to engineers and partner with business stakeholders to turn manufacturing metrics into data models and pipelines.
What they ask for
- Bachelor's degree in Computer Science, Management Information Systems, a related technical field, or equivalent practical experience.
- 8 years of experience with general-purpose programming languages such as Python, Java, Go, or C++.
- 8 years of experience in enterprise application development and data engineering, including data pipelines, databases, and web services.
- 5 years of experience leading system architecture, integrating third-party systems, and mentoring engineering teams.
Nice to have
- Master's degree in Computer Science, MIS, or a related technical field, or equivalent practical experience.
- Supply chain, hardware manufacturing, or MES expertise; AI/ML application experience; full-stack or cloud-native data processing experience; or secure data-sharing, IAM, and governance experience.
Pay
$163000 - $236000 USD base salary
Tools
Python, Java, Go, C++, REST APIs, FastAPI, REST/SOAP APIs, OAuth2/OIDC
AI focus
Builds an AI-ready data ecosystem and pipelines optimized for predictive machine learning models, including factory stall and yield anomaly prediction, and downstream AI agents.
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