Job Description

Minimum qualifications

• Bachelor's degree in Computer Science, Management Information Systems, a related technical field, or equivalent practical experience.
• 8 years of experience working with general-purpose programming languages (e.g., Python, Java, Go, or C++).
• 8 years of experience in enterprise application development and data engineering, including designing data pipelines, databases and web services (e.g., REST/SOAP APIs)
• 5 years of experience leading system architecture, integrating third-party systems, and mentoring engineering teams.

Preferred qualifications

• Master's degree in Computer Science, MIS or related technical field or equivalent practical experience.
• Domain expertise within supply chain operations, hardware manufacturing data (e.g., FPY, Cycle Time), or MES.
• Experience developing AI/ML-enabled applications or integrating cloud-native AI services and designing data architectures optimized for ML workloads.
• Experience with full-stack development, modern web frameworks, and cloud-native data processing pipelines.
• Experience designing secure external data-sharing portals, enterprise Identity and Access Management (OAuth2/OIDC) integration, and data governance.
• Exceptional cross-functional communication skills, with a proven ability to work face-to-face with internal business stakeholders to translate complex requirements into technical solutions.

About the job

You lead all aspects of web development, requirements gathering, software development, testing, documentation, training, implementation, ongoing support, and maintenance for both in-house and customer-facing web applications. You will work closely with a cross-functional team of Googlers using UI and functional specs/prototypes as input. You are empowered to act like an owner, take action and innovate to meet user and customer needs.

As an Application Engineer Lead, you are the engineers' engineer, passionate about building technology solutions that enhance critical business processes. You will own the end-to-end technical architecture and full-stack software development for a next-generation predictive supplier quality and risk intelligence platform.

In this role, you will bridge complex hardware manufacturing telemetry across critical data center programs with scalable enterprise cloud services. You will architect an AI-ready data ecosystem that enables proactive visibility into global supply chain risks, provide technical direction for a dedicated pod of application and data engineers, partner cross-functionally with Global Supply Chain Managers, and deploy tooling to enable proactive visibility into global supply chain risks.

The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.

We're the driving team behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $163000 - $236000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

• Architect, build, and deploy scalable enterprise applications and secure external-facing partner portals, operating as a technical leader (coding 50-70% of the time).
• Design and implement robust web services and synchronous microservices (e.g., REST APIs, FastAPI) to integrate external contract manufacturing systems with internal data warehouses.
• Architect an AI-ready data foundation and semantic serving layer across upstream supplier manufacturing telemetry; automated pre-flight quality validation and feature pipelines optimized for predictive machine learning models (e.g., predicting factory stalls and yield anomalies) and downstream AI agents.
• Provide technical direction for a cross-functional pod of extended workforce engineers, establishing code quality standards, leading system design reviews, and managing the end-to-end application architecture.
• Partner closely with internal customers, specifically Hardware Quality Engineers and Supply Chain Managers, to translate physical manufacturing metrics into robust dimensional data models and automated pipelines.