HPC Systems Manager

KLA is a global leader in diversified electronics for the semiconductor manufacturing ecosystem. Virtually every electronic device in the world is produced using our technologies. No laptop, smartphone, wearable device, voice-controlled gadget, flexible screen, VR device or smart car would have made it into your hands without us. KLA invents systems and solutions for the manufacturing of wafers and reticles, integrated circuits, packaging, printed circuit boards and flat panel displays. The innovative ideas and devices that are advancing humanity all begin with inspiration, research and development. KLA focuses more than average on innovation and we invest 15% of sales back into R&D. Our expert teams of physicists, engineers, data scientists and problem-solvers work together with the world's leading technology providers to accelerate the delivery of tomorrow's electronic devices. Life here is exciting and our teams thrive on tackling really hard problems. There is never a dull moment with us.

With over 40 years of semiconductor process control experience, chipmakers around the globe rely on KLA to ensure that their fabs ramp next-generation devices to volume production quickly and cost-effectively. Enabling the movement towards advanced chip design, KLA's Global Products Group (GPG), which is responsible for creating all of KLA's metrology and inspection products, is looking for the best and the brightest research scientist, software engineers, application development engineers, and senior product technology process engineers. The LS-SWIFT Division of KLA's Global Products Group provides patterned wafer inspection systems for high-volume semiconductor manufacturing. Its mission is to deliver market-leading cost of ownership in defect detection for a broad range of applications in the production of semiconductors. Customers from the foundry, logic, memory, automotive, MEMS, advanced packaging and other markets rely upon high-sample wafer inspection information generated by LS-SWIFT products. LS (Laser Scanning) systems enable cost-effective patterned wafer defect detection for the industry's most sophisticated process technologies deployed in leading-edge foundry, logic, DRAM, and NAND fabs. SWIFT (Simultaneous Wafer Inspection at Fast Throughput) systems deliver all-wafer-surface (frontside, backside, and edge) macro inspection that is critical for automotive IC, MEMS, and advanced packaging processes as well as foundry/logic and memory fabs. LS-SWIFT operates from a global footprint that includes the US, Singapore, India and Germany, and serves a worldwide customer base across Asia, Europe and North America.

Job Description/Preferred Qualifications

As anSystem Design Engineering Managerspecializing inAI Systems and High-Performance Computing (HPC), you'll play a pivotal role in shaping the future of AI-driven solutions. Your leadership will drive innovation, optimize performance, and foster collaboration across cross-functional teams. Let's delve into the details:

Key Responsibilities:

Technical Leadership:

• Lead a team of engineers, and system architects to develop the platform for DL training and inference

• Define the technical vision, strategy, and roadmap for DL/AI systems within the HPC domain.AI Infrastructure Design and Optimization:

• Collaborate with hardware and software teams to design and optimize AI infrastructure.

• Ensure seamless integration of AI workloads with existing HPC clusters.

GPU Cluster Management and Scalability:

• Oversee the management of GPU-based clusters.

• Scale AI infrastructure to handle large-scale training and inferencing workloads..

Performance Tuning and Benchmarking:

• Drive performance improvements by analyzing bottlenecks and optimizing system components.

• Benchmark AI models and algorithms on HPC clusters.

Collaboration and Communication:

• Work closely with product managers, researchers, and stakeholders to align AI initiatives with business goals.

• Communicate technical progress, risks, and opportunities to senior leadership.

Minimum Qualifications

Experience:

• Proven track record in managing engineering teams, preferably in AI, HPC, or related fields.

• Familiarity with AI frameworks (TensorFlow, PyTorch, etc.) and HPC tools (Slurm, OpenMPI, etc.).

Technical Proficiency:

• Strong understanding of GPU architectures, CUDA programming, and parallel computing.

• Knowledge of containerization (Docker, Kubernetes) and cloud-based AI deployments.

Leadership Skills:

• Ability to mentor and develop team members.

• Excellent decision-making, problem-solving, and conflict resolution skills.

Communication:

• Effective communication across technical and non-technical stakeholders.

• Experience presenting technical concepts to executive leadership.

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