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. 
• 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. 

          

Job Type:
Experienced Hire

Shift:
Shift 1 (India)

Primary Location:
India, Bangalore

Additional Locations:

Posting Statement:
All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.
Position of Trust
N/A
Work Model for this Role
This role will require an on-site presence. * Job posting details (such as work model, location or time type) are subject to change.
*

ADDITIONAL INFORMATION: Intel is committed to Responsible Business Alliance (RBA) compliance and ethical hiring practices. We do not charge any fees during our hiring process. Candidates should never be required to pay recruitment fees, medical examination fees, or any other charges as a condition of employment. If you are asked to pay any fees during our hiring process, please report this immediately to your recruiter.

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. 
• 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. 

          

Job Type:
Experienced Hire

Shift:
Shift 1 (India)

Primary Location:
India, Bangalore

Additional Locations:

Posting Statement:
All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.
Position of Trust
N/A
Work Model for this Role
This role will require an on-site presence. * Job posting details (such as work model, location or time type) are subject to change.
*

ADDITIONAL INFORMATION: Intel is committed to Responsible Business Alliance (RBA) compliance and ethical hiring practices. We do not charge any fees during our hiring process. Candidates should never be required to pay recruitment fees, medical examination fees, or any other charges as a condition of employment. If you are asked to pay any fees during our hiring process, please report this immediately to your recruiter.