Principal Software Engineer Manager (AI Infrastructure) - North America Software Center

TSMC•Vancouver, WA

10d•$166,800 - $241,800•Onsite

About The Position

TSMC Washington’s North America Software Center is seeking a Principal Software Engineer Manager to build and lead an engineering team responsible for the Enterprise AI Infrastructure Platform. This dual-impact role combines technical leadership in distributed systems and AI platforms with people management. The position will architect scalable, high-throughput AI infrastructure supporting internal and external foundation models, and collaborate with engineering teams across North America and Taiwan. The role sets the technical direction for AI platform capabilities, including an enterprise AI gateway, multi-model inference, GPU infrastructure, AI governance, and observability. It also leads the design and delivery of infrastructure for LLM serving, GPU scheduling, model routing and lifecycle management, RAG, vector databases, and AI orchestration, along with highly available distributed services and cloud-native platform engineering. This position is based on-site in Vancouver, Washington, during core business hours, Monday through Friday, 8 a.m. to 5 p.m. Additional hours may be required. Applicants must be authorized to work in the United States; visa sponsorship is not available. Employment is contingent on passing a background check and drug screening.

Requirements

BS, MS, or PhD in Computer Science or a related field.
12 or more years of software engineering experience.
5 or more years leading architecture for distributed systems or cloud infrastructure.
3 or more years of hands-on experience designing enterprise AI platforms.
3 or more years of formal people-management experience managing teams of senior or staff engineers.
Strong technical communication skills for collaborating with global cross-functional teams.
Strong experience with LLM inference, enterprise AI gateways, AI serving infrastructure, GPU scheduling, multi-model AI architecture, and model lifecycle management.
Deep expertise in Kubernetes, Docker, Helm, ArgoCD, and service mesh.
Experience with AWS, Azure, or GCP.
Strong programming skills in one or more of Go, Python, or Java.
Experience with REST APIs, microservices, and SDKs.
Legal authorization to work in the United States; the company cannot provide or take over sponsorship of an employment visa.
Ability to pass a background check and drug screening.

Nice To Haves

Experience with the NVIDIA GPU ecosystem, vLLM, TensorRT-LLM, Triton Inference Server, Ray, Kubeflow, or MLflow.

Responsibilities

Hire, lead, and develop a team of AI infrastructure and distributed systems engineers.
Foster technical excellence, continuous learning, innovation, and psychological safety.
Set quarterly engineering roadmaps, establish agile delivery standards, and ensure high operational reliability.
Define the long-term technical direction for the AI platform, enterprise AI gateway, multi-model inference platform, GPU infrastructure, AI governance, and AI observability.
Design scalable platforms for LLM serving, GPU scheduling, model routing and lifecycle management, RAG infrastructure, vector databases, and AI orchestration.
Lead architecture for highly available AI services, multi-region deployment, disaster recovery, service mesh, distributed caching, event-driven architecture, and global load balancing.
Drive engineering best practices for Kubernetes, Platform-as-a-Service, and AI deployment automation.
Define technical strategy across multiple engineering teams and lead architecture reviews.
Mentor senior engineers and drive engineering excellence.
Influence technical decisions across North America and Taiwan.
Partner with product managers, architects, and engineering leaders to define long-term roadmaps.