Job Description

Company

Qualcomm India Private Limited

Job Area

Engineering Group, Engineering Group > Software Engineering

General Summary

As a leading technology innovator, Qualcomm pushes the boundaries of what is possible to enable next-generation intelligent computing, AI, connectivity, and low-power embedded technologies.

We are seeking an experienced AI Systems Architect to define, prototype, and optimize next-generation AI computing systems across heterogeneous hardware and software platforms. This role will focus on understanding how AI workloads utilize system resources and developing architectural solutions that maximize performance, power efficiency, scalability, and overall platform utilization.

The ideal candidate will work across hardware, firmware, runtime, compiler, and application layers to evaluate system-level tradeoffs and prototype innovative architectures for AI workloads. The role requires strong expertise in AI workloads, heterogeneous computing, system performance analysis, and architectural modeling.

Responsibilities

Define and evaluate system architectures for AI platforms incorporating heterogeneous compute elements such as CPU, NPU, GPU, Memory interconnects and subsystems.
Experience with profiling/modeling tools to understand optimal use of resources for getting best system performance
Develop system-architecture prototypes to evaluate - workload partitioning across compute engines, data movement and memory traffic, compute and memory bottlenecks, scheduling and resource allocation, power, thermal, and performance tradeoffs
Analyze AI workloads to determine the best utilization of available architectural resources across available compute and memory resources
Investigate end-to-end AI execution pipelines, including -Model compilation and graph partitioning, Runtime scheduling, Kernel selection, Memory allocation, Data transfers, Synchronization, Accelerator utilization
Build analytical models, simulators, emulators, and proof-of-concept implementations for assessing architectural concepts before hardware availability.
Evaluate the impact of system-level parameters such as Compute throughput, Memory bandwidth, Cache and local memory capacity, Interconnect bandwidth and latency, DMA and data-transfer efficiency, Accelerator occupancy
Identify performance bottlenecks and recommend architectural improvements based on workload characterization, profiling data, and benchmark results.
Collaborate with hardware architects, AI accelerator designers, compiler teams, runtime engineers, firmware developers, performance engineers, and product teams.
Define workload-driven architectural requirements and translate them into platform-level specifications.
Develop technical proposals, architecture studies, tradeoff analyses, and performance projections for future AI platforms.

Skills And Experience

Bachelor's or Master's degree in Computer engineering, Electrical Engineering, Computer Science, or a related field.

Strong programming skills in C, C++, and Python.
Experience in one or more of the following areas:

AI or machine-learning systems
Computer architecture
Heterogeneous computing
SoC architecture
Accelerator architecture
High-performance computing
Embedded systems
System performance analysis

3-7 years of experience in hardware focused system optimization role (hiring across multiple levels).
Strong understanding of computer architecture concepts
Ability to develop quantitative models and make architecture recommendations based on empirical data.
Excellent written and verbal communication skills.
Demonstrated ability to learn quickly, think critically, and adapt in a fast-paced and rapidly evolving technology environment.

Good to have

Experience with AI specific performance optimization would be preferred. Hands-on experience optimizing AI models for performance, power, and accuracy on hardware accelerators such as CPUs, GPUs, and NPUs.
Experience using processor-specific optimization tools, libraries, and profiling technologies to improve AI performance on CPUs, GPUs, and NPUs is a plus.
Strong understanding of modern machine learning architectures, including CNNs, RNNs, Transformers, and ensemble methods would be good to have.
Proficiency with AI frameworks and tooling such as PyTorch, TensorFlow, ONNX Runtime, and MLIR would be good to have.
Experience with transformer-based models such as BERT, GPT, T5, and Llama. Experience developing, optimizing, or deploying large language models (LLMs) or foundation models is a strong plus.

Minimum Qualifications

Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience.

OR

Master's degree in Engineering, Information Systems, Computer Science, or related field and 1+ year of Software Engineering or related work experience.

OR

PhD in Engineering, Information Systems, Computer Science, or related field.

2+ years of academic or work experience with Programming Language such as C, C++, Java, Python, etc.

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