Intel AI System Performance Engineer Recruitment 2026 – Bangalore
Intel is inviting applications for the position of AI System Performance Engineer in Bangalore, India. This is a full-time College Grad opportunity for candidates interested in AI workloads, GPU architecture, system performance modelling, and simulation frameworks.
The selected candidate will work on analytical models for AI training and inference systems, evaluate performance across different hardware configurations, and help identify architectural bottlenecks. Candidates with a strong foundation in Python, computer architecture, and large language model (LLM) workloads can consider applying.
Official Application Link: Apply for Intel AI System Performance Engineer
Intel Recruitment 2026 – Job Overview
| Company | Intel |
|---|---|
| Job Role | AI System Performance Engineer |
| Job ID | JR0288211 |
| Job Location | Bangalore, India |
| Job Type | Full Time |
| Job Category | College Grad |
| Work Model | Hybrid |
| Shift | Shift 1 – India |
| Qualification | B.Tech or Master’s degree in a relevant engineering or computer science field |
| Experience | College graduate or 1+ years in a relevant field |
| Salary | Not disclosed in the official job description |
| Application Mode | Online |
About the Role
The AI System Performance Engineer will help develop analytical models that estimate end-to-end performance for AI training and inference systems. The role involves understanding serving strategies, parallelism, collective communication, and interconnect behaviour to identify performance limitations and recommend suitable system configurations for Intel architecture.
The position requires a hands-on and self-driven approach. Candidates should be comfortable learning unfamiliar workloads, tools, and architectures and applying that knowledge to performance modelling and simulation.
Key Responsibilities
- Enable new AI workloads in analytical simulators and model emerging multi-GPU deployment, parallelization, and communication strategies.
- Build analytical models for proposed GPU architectures, including GPU cores, memory hierarchy, and training or inference deployment scenarios.
- Estimate performance, identify architectural bottlenecks, and provide recommendations for system improvements.
- Integrate and modify open-source simulators or analytical models for Intel-specific architecture.
- Conduct what-if analysis by varying core count, memory and cache capacity, bandwidth, fabric bandwidth, GPU interconnect, and deployment options.
- Recommend optimized system configurations and architectures based on performance analysis.
Eligibility Criteria
Applicants should meet the following qualifications mentioned in the official job description:
- Education: B.Tech or Master’s degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field.
- Experience: College graduate or candidates with 1+ years of experience in AI workload and GPU architecture modelling or simulation, or a similar field.
- Programming: Strong proficiency in Python, including the ability to build modular and maintainable analytical models and simulation frameworks.
- Computer Architecture: In-depth understanding of GPU and CPU architecture, ALU/SM cores, caches, and different memory types and hierarchies.
- AI Workloads: In-depth understanding of LLM workloads, including Transformer, MoE, Attention, MLA, LLaMA, DeepSeek, Qwen, robotic workloads, and kernel internals.
Good-to-Have Skills
The following skills are listed as desirable rather than mandatory:
- Distributed training and inference, including data, pipeline, tensor, and expert parallelism strategies.
- Understanding of communication patterns when scaling workloads across multiple GPUs.
- Knowledge of LLM training and inference mechanics, including prefill versus decode, KV cache, batching policies, and quantization.
- Exposure to TRT-LLM, vLLM, or SGLang.
- Familiarity with Git/GitHub, CI/CD, and automated workflows.
- Understanding of power fundamentals and power-performance co-optimization.
Salary Details
Intel has not disclosed the salary or compensation range for this AI System Performance Engineer position in the provided official job description. Candidates should refer to the official recruitment page or the recruiter for confirmed compensation details.
Work Location and Work Model
The primary work location is Intel’s Bangalore site in India. The role is eligible for a hybrid work model, which allows employees to divide their time between working on-site and off-site. The official posting states that details such as location and work model may be subject to change.
How to Apply for Intel AI System Performance Engineer
- Visit the official Intel careers application page using the link below.
- Review the job description, qualifications, and application details carefully.
- Prepare an updated resume highlighting Python, computer architecture, AI workload modelling, simulation, and relevant projects or experience.
- Submit your application through Intel’s official recruitment portal and follow the instructions provided there.
Apply Online: Intel AI System Performance Engineer – Official Application
Important: Intel states that it does not charge recruitment fees, medical examination fees, or other charges during its hiring process. Do not pay anyone claiming to offer recruitment or selection for this position.
Frequently Asked Questions (FAQs)
1. Where is the Intel AI System Performance Engineer job located?
The primary location is Bangalore, India. The role is eligible for Intel’s hybrid work model.
2. What qualification is required for this Intel job?
Candidates need a B.Tech or Master’s degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field, along with the relevant AI workload or GPU architecture modelling background specified in the job description.
3. Is the salary mentioned for this position?
No. The official job description provided does not disclose the salary or compensation range.
Share this job opportunity with eligible candidates who are interested in AI systems, GPU architecture, and performance engineering.