NextSilicon is reimagining high-performance computing (HPC & AI). Our accelerated compute solutions leverage intelligent adaptive algorithms to vastly accelerate supercomputers, driving them forward into a new generation. We have developed a novel software-defined hardware architecture that is achieving significant advancements in both the HPC and AI domains.

At NextSilicon, everything we do is guided by three core values:

• Professionalism: We strive for exceptional results through professionalism and unwavering dedication to quality and performance.
• Unity: Collaboration is key to success. That's why we foster a work environment where every employee can feel valued and heard.
• Impact: We're passionate about developing technologies that make a meaningful impact on industries, communities, and individuals worldwide.
• impact on industries, communities, and individuals worldwide.

We are seeking a highly skilled AI Application Team Lead to build and lead a team responsible for developing, running, and optimizing large-scale AI workloads on NextSilicon’s AI hardware platform. This role focuses on benchmarking state-of-the-art models (e.g., LLaMA, DeepSeek), executing MLPerf suites, analyzing system-level performance, and driving cross-stack optimizations across hardware, runtime, and software frameworks.

The ideal candidate combines strong technical depth in AI/ML systems, hands-on experience with LLM workloads, and leadership capability to guide a high-performance engineering team.

• 5+ years of experience in AI/ML engineering, performance optimization, or ML systems.
• Deep understanding of LLM architectures, training & inference mechanics, and modern ML frameworks.
• Strong proficiency in PyTorch ecosystem, with a specific focus on performance tuning via Triton, Cuda or MLIR-based compiler frameworks.
• Hands-on expertise profiling and optimizing kernels (GEMM, attention, softmax, token pipelines).
• Demonstrated experience running or tuning MLPerf or similar large-scale benchmarks.
• Strong Python and C++ development skills.
• Proven leadership experience: mentoring, guiding, or managing engineers.

• Lead and mentor a team of AI application and performance engineers.
• Run and optimize AI workloads (LLaMA, DeepSeek, etc.) and execute MLPerf benchmarks.
• Analyze end-to-end performance and identify HW/SW bottlenecks.
• Develop optimization strategies across models, kernels, frameworks, and runtime.
• Build profiling, debugging, and validation tools for large-scale AI workloads.
• Collaborate with hardware, compiler, and device software teams to improve performance.