AI HPC Manager

Established in 1987 and headquartered in Taiwan, TSMC pioneered the pure-play foundry business model with an exclusive focus on manufacturing its customers' products. As of 2024, TSMC serves more than 500 customers and manufactures over 11,000 products for high-performance computing, smartphones, the Internet of Things (IoT), automotive, and digital consumer electronics. It is the world's largest provider of logic ICs, with an annual capacity of 16 million 12-inch equivalent wafers. TSMC operates fabs in Taiwan as well as manufacturing subsidiaries in Washington State, Japan and China, and the Company began construction on a specialty technology fab in Dresden, Germany, in 2024. In Arizona, TSMC is building three fabs, with the first starting 4nm production in 2025, the second by 2028, and the third by the end of the decade.

Job Responsibilities:

An AI (Artificial Intelligence) HPC (High-Performance Computing) architect is responsible for designing, developing, and implementing high-performance computing solutions with a specific focus on artificial intelligence and machine learning workloads. Their role involves a combination of expertise in AI algorithms, HPC technologies, and system architecture.

Job description of AI HPC Architect:

1. Solution Design: Collaborate with stakeholders, including data scientists, researchers, and IT teams, to understand their requirements and translate them into effective AI HPC solutions. Design architectures that optimize performance, scalability, and efficiency for AI workloads.

2. HPC Infrastructure Planning: Evaluate and select appropriate hardware, networking, and storage resources to support AI computations at scale. This involves considering factors such as parallel processing, GPU (Graphics Processing Unit) acceleration, interconnect technologies, and storage systems.

3. Algorithm Optimization: Work closely with data scientists and machine learning experts to identify opportunities for algorithm optimization. Apply techniques like parallel processing, distributed computing, and GPU acceleration to improve the performance and efficiency of AI models.

4. Performance Tuning: Optimize the performance of AI workloads by fine-tuning system configurations, resource allocation, and workload management. Identify and resolve bottlenecks related to computation, memory, storage, or network bandwidth.

5. Scalability and Resilience: Design solutions that can scale to handle large-scale AI workloads and accommodate future growth. Ensure high availability and fault tolerance by implementing redundancy, load balancing, and failover mechanisms.

6. Integration and Deployment: Collaborate with software engineers and DevOps teams to integrate AI models and algorithms into production environments. Develop deployment strategies and workflows for efficient deployment and management of AI HPC systems.

7. Research and Innovation: Stay up to date with the latest advancements in AI, machine learning, and HPC technologies. Identify and evaluate emerging technologies, frameworks, and tools to enhance the performance and capabilities of AI HPC systems.

8. Documentation and Communication: Document system designs, configurations, and performance optimizations. Communicate complex technical concepts and recommendations effectively to both technical and non-technical stakeholders.

Job Qualifications:

1. Strong Background in AI and Machine Learning: A deep understanding of artificial intelligence and machine learning concepts, algorithms, and frameworks is essential. Experience in developing and deploying AI models is highly valuable.

2. Expertise in High-Performance Computing: In-depth knowledge of high-performance computing architectures, technologies, and best practices is crucial. Familiarity with HPC frameworks, parallel processing, distributed computing, and GPU acceleration is important for optimizing AI workloads.

3. System Architecture and Design: Proficiency in designing scalable and efficient system architectures is necessary. Experience in selecting and configuring hardware components, networking technologies, and storage systems for high-performance computing is desirable.

4. Programming and Scripting Skills: Proficiency in programming languages such as Python, C++, or Java is important for implementing and optimizing AI algorithms. Knowledge of scripting languages like Bash or PowerShell is beneficial for automation and system management tasks.

5. HPC Tools and Frameworks: Familiarity with HPC tools and frameworks, such as MPI (Message Passing Interface), OpenMP, CUDA, or OpenCL, is valuable. Understanding how to leverage these tools for parallel computing and GPU acceleration is advantageous.

6. Performance Optimization: Experience in performance tuning and optimization techniques for large-scale computing systems is essential. Knowledge of profiling tools, benchmarking, and workload management is valuable for identifying and resolving performance bottlenecks.

7. System Administration: Understanding system administration principles and practices is beneficial. Knowledge of Linux or UNIX-based operating systems, system monitoring tools, and cluster management frameworks is advantageous.

8. Communication and Collaboration: Strong communication skills are crucial for collaborating with cross-functional teams, including data scientists, researchers, software engineers, and IT professionals. The ability to convey complex technical concepts to both technical and non-technical stakeholders is important.

9. Continuous Learning: The field of AI and HPC is rapidly evolving, so a passion for continuous learning and staying updated with the latest advancements is important. Keeping up with research papers, attending conferences, and participating in relevant communities can contribute to professional growth.

Fostering a global inclusive workplace reflects TSMC's core values and business philosophy and is essential for our future success. Our commitment to global inclusive workplace allows us to create an environment where every employee, regardless of gender, age, disability, religion, race, ethnicity, nationality, political affiliation, or sexual orientation, can bring their unique perspective and experiences to work, enabling us to drive profitability, increase productivity, and unleash innovation. We strive to create a workplace that is equitable and accessible to all employees. We are committed to fostering an inclusive culture where every employee feels valued and empowered to contribute to our mission and provide excellent service to our global customers.