AI Application Team Lead at NextSilicon

Givatayim, Tel-Aviv District, Israel -

Full Time

Start Date

Immediate

Expiry Date

21 Mar, 26

Salary

0.0

Posted On

21 Dec, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI/ML Engineering, Performance Optimization, LLM Architectures, Training Mechanics, Inference Mechanics, Modern ML Frameworks, PyTorch, Performance Tuning, Triton, Cuda, MLIR, Kernel Profiling, Optimizing Kernels, MLPerf, Python, C++, Leadership

Industry

Computer Hardware Manufacturing

Description

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. Requirements 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. Responsibilities 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.

Responsibilities

Lead and mentor a team of AI application and performance engineers while running and optimizing AI workloads. Analyze performance and develop optimization strategies across models, kernels, frameworks, and runtime.