Technical Lead, HPC Software Systems

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 worlds leading technology providers to accelerate the delivery of tomorrows 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 KLAs 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 KLAs 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 industrys 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. Key Responsibilities:

• Design, develop, and optimize software infrastructure for high-performance computing (HPC) systems tailored to AI and machine learning applications.
• Linux System Programming: Process management, multithreading, IPC, Sockets, shared memory, distributed file systems, memory management
• Linux Debugging: gdb, core dump analysis, strace, perf, valgrind
• Performance Debugging: CPU, GPU profiling, memory profiling, I/O bottleneck analysis, NUMA awareness, throughput analysis
• Implement robust and scalable solutions to manage data processing, storage, and retrieval for AI workloads.
• Participate in code reviews, testing, and debugging to maintain high-quality software standards.
• Stay updated with the latest advancements in AI, HPC, and software engineering to drive innovation within the team.

Qualifications:

• Bachelor's or Master's degree in Computer Science, Computer Engineering, or a related field.
• Strong programming skills in languages such as Python, C++, and Java.
• Proven experience in software engineering, with a focus on distributed systems, HPC or AI infrastructure development.
• Proven experience in Linux systems programming and familarity in using profiling tools
• Excellent problem-solving skills and the ability to work in a fast-paced, collaborative environment.
• Passion for AI and machine learning, with a keen interest in developing infrastructure that drives these technologies forward.

Preferred Qualifications:

• Experience in developing large scale distribution applications.
• Knowledge of containerization and orchestration tools (e.g., Docker, Kubernetes).
• Understanding of data engineering and big data technologies (e.g., Hadoop, Spark).

• Bachelor's or Master's degree in Computer Science, Computer Engineering, or a related field.
• Strong programming skills in languages such as Python, C++, and Java.