AI Software Engineer - AI Frameworks

About the role

Job Details:

Job Description:

Overview:

We are seeking a dynamic and passionate AI software engineer to join Intel's Data Center and AI group (DCAI). In this role, you will design and develop optimizations for high-performance AI serving and training frameworks such as SGLang, vLLM, and PyTorch. You will contribute to Intel's AI framework software stack and help enable and optimize state-of-the-art solutions for Intel's AI accelerators and next-generation GPUs. This position requires deep expertise in framework internals, scheduling, memory management, and kernel optimization for production AI workloads.

Roles and Responsibilities:

- Design and develop custom GPU kernels for AI framework operations including attention mechanisms, collective operations, and computation primitives to optimize performance on Intel accelerators and GPUs.

- Optimize and enhance framework components including scheduling, memory management, and execution pipeline architecture to improve throughput, latency, and resource utilization.

- Write and integrate high-performance kernels using GPU programming frameworks to maximize throughput and minimize latency.

- Identify and implement optimization opportunities in the framework stack for production AI workloads.

- Collaborate with the open-source community (SGLang, vLLM, PyTorch, and others), participate in upstream development, and integrate new innovations into production software.

- Debug and optimize complex multi-layered systems involving kernel execution, memory hierarchies, and distributed AI scenarios.

Qualifications:

Qualifications:

- Bachelor's degree (B.Tech) or Master's degree (M.S./M.Tech) in Computer Science, Electronics and Communications Engineering (ECE), or a related field, with 6 to 12 years of professional experience.

- Advanced proficiency in C++ (C++14/17) and intermediate skills in Python and parallel programming.

- Proven, hands-on experience writing and optimizing GPU kernels. Demonstrated expertise in kernel development for machine learning operations such as GEMM, attention mechanisms (including Flash Attention), collective operations, and sampling.

- In-depth, hands-on experience with AI frameworks—SGLang, vLLM, PyTorch, or similar production-grade serving and training systems. Deep understanding of their internal architecture, scheduling mechanisms, and memory management strategies is essential.

- Strong knowledge of AI inference and training concepts including batching strategies, memory optimization, distributed execution, and performance tuning.

- Practical experience optimizing and deploying AI models at scale in production environments.

- Strong ability to debug complex issues in multi-layered software systems, performance profiling, and bottleneck identification in AI frameworks.

- Solid understanding of computer architecture, GPU execution models, memory hierarchies, and hardware-software co-optimization techniques.