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Intel

Pioneers microprocessors, CPUs for PCs

AI Software Engineer - AI Frameworks

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeBengaluru, Karnataka, IndiaNo salary listed

About the Job

Requirements

  • 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, including 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 such as SGLang, vLLM, PyTorch, or similar production-grade serving and training systems, including understanding of internal architecture, scheduling mechanisms, and memory management strategies.
  • 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.
  • Ability to debug complex issues in multi-layered software systems, perform performance profiling, and identify bottlenecks in AI frameworks.
  • Understanding of computer architecture, GPU execution models, memory hierarchies, and hardware-software co-optimization techniques.
  • Experience working on frameworks and platforms deployed to production with high availability and performance requirements.
  • Experience collaborating with cross-functional, geographically distributed teams.

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, 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.

Desired 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.

About the Company

Intel

Pioneers microprocessors, CPUs for PCs

Intel designs and manufactures semiconductor chips, with a focus on microprocessors for personal computers, servers, and other devices. Its core product is the CPU on a single silicon chip, which executes instructions, handles arithmetic and logic operations, and coordinates the work of other computer components. Intel originated in memory chips but shifted decisively to microprocessors in the 1980s, becoming a central supplier for the PC era after the IBM partnership and its famous x86 processor line. This shift, large-scale manufacturing, and close ties with computer makers set Intel apart from competitors who remained focused on memory or other components. The company aims to power computing by delivering high-performance, energy-efficient silicon solutions that drive a wide range of computing devices and applications.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1999

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