AI Software Engineer - AI Frameworks

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.

• Experience working on frameworks and platforms that have been deployed to production with high availability and performance requirements.

• Excellent communication skills and experience collaborating with cross-functional, geographically distributed teams.

Preferred Qualifications:

• Extensive experience developing production-grade kernels with CUDA, CUTLASS, or Triton for various AI operations.

• Published or open-source contributions demonstrating kernel optimization expertise.

• Knowledge of compiler algorithms for heterogeneous systems and kernel fusion optimizations.

• Contributions to or deep familiarity with SGLang, vLLM, PyTorch, or other open-source AI frameworks.

• Experience with distributed AI workloads, multi-GPU scheduling, and optimization across heterogeneous hardware.

• Understanding of quantization techniques, mixed-precision inference and training, and their integration into AI frameworks.

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Posting Statement:

Position of Trust

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