Generative AI Software Engineer

2 Hours ago • 2 Years +

Research Development

Job Description

Qualcomm AI Research is seeking a Generative AI Software Engineer to advance AI capabilities across devices. The role involves designing and developing AI tools, models, and software for efficient deployment of quantized neural networks on Qualcomm hardware, optimizing for performance. Key responsibilities include deploying AI models using QNN SDK, implementing LLM/LMM inference drivers, developing debugging tools, building automated test suites, and collaborating with cross-functional teams to analyze test results and provide feedback.

Good To Have:

Proficiency in Python and C/C++.
Strong skills in software design, development, and debugging.
Hands-on experience with generative AI models, including LLMs and LVMs.
Solid understanding of deep learning and experience with popular frameworks such as PyTorch, transformer and the Hugging Face ecosystem.
Knowledge of LLM/LMM inference engines, such as llama.cpp or ExecuTorch.
Experience working with the Qualcomm QNN SDK is highly desirable.
Experience working with the Qualcomm inference driver Genie is highly desirable.
Knowledge of neural network quantization techniques is a significant advantage.
Experience with Android programming is a plus.
Experience using AI coding assistants such as Claude code, Codex, or Cursor is a plus.

Must Have:

Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field.
At least 2 years of relevant professional experience.
Design and develop end-to-end AI tools, models, and software for efficient deployment of quantized neural networks on Qualcomm hardware.
Optimize performance for speed, latency, memory, and power consumption.
Deploy AI models to Qualcomm CPU/NPU/GPU using the QNN SDK toolchain.
Implement and optimize inference drivers for large language models (LLM) and large multimodal models (LMM).
Develop debugging and profiling tools, as well as Qualcomm SDK solutions, to solve software issues and facilitate rapid on-device deployment of quantized models.
Build and maintain automated test suites leveraging modern testing tools and frameworks.
Collaborate closely with cross-functional teams to gather requirements and design development/test approaches.
Analyze test results and provide actionable feedback to the core development team.

Perks:

World-class health benefit options providing world-class coverage to employees and their eligible dependents.
Programs designed to help employees build and prepare for a financially secure future.
Self and family resources to build emotional/mental strength and resilience, and define purpose.
Wellbeing programs and resources to support employees to Live+Well and Work+Well.
Continuous learning and development programs.
Tuition reimbursement.
Mentorships.

Add these skills to join the top 1% applicants for this job

cross-functional

problem-solving

cpp

game-texts

quality-control

test-suites

pytorch

deep-learning

neural-networks

python

machine-learning

Job Posting Date

2025-10-09

Additional Job Posting Location

Xian CHN

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Job Area:

Engineering Group, Engineering Group > Machine Learning Engineering

General Summary:

About us:

We are Qualcomm AI Research that are advancing AI to make its core capabilities – perception, reasoning, and action – ubiquitous across devices. Our mission is to make breakthroughs in fundamental AI research and scale them across industries. By bringing together some of the best minds in the field, we're pushing the boundaries of what's possible and shaping the future of AI. Welcome to visit our website at AI Research Areas | Intelligence on Devices | Qualcomm

Job area: Engineering – Machine Learning Engineering

Minimum Qualifications:

Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field
At least 2 years of relevant professional experience

Key Responsibilities:

Design and develop end-to-end AI tools, models, and software to enable efficient deployment of quantized neural networks on cutting-edge Qualcomm hardware, with a strong focus on optimizing performance for speed, latency, memory, and power consumption
Deploy AI models to Qualcomm CPU/NPU/GPU using the QNN SDK toolchain
Implement and optimize inference drivers for large language models (LLM) and large multimodal models (LMM)
Develop debugging and profiling tools, as well as Qualcomm SDK solutions, to solve software issues and facilitate rapid on-device deployment of quantized models
Build and maintain automated test suites leveraging modern testing tools and frameworks
Collaborate closely with cross-functional teams to gather requirements and design development/test approaches
Analyze test results and provide actionable feedback to the core development team

Preferred Skills and Experience:

Proficiency in Python and C/C++
Strong skills in software design, development, and debugging
Hands-on experience with generative AI models, including LLMs and LVMs
Solid understanding of deep learning and experience with popular frameworks such as PyTorch, transformer and the Hugging Face ecosystem
Knowledge of LLM/LMM inference engines, such as llama.cpp or ExecuTorch
Experience working with the Qualcomm QNN SDK is highly desirable
Experience working with the Qualcomm inference driver Genie is highly desirable
Knowledge of neural network quantization techniques is a significant advantage
Experience with Android programming is a plus
Experience using AI coding assistants such as Claude code, Codex, or Cursor is a plus

Minimum Qualifications:

• Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 4+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.

Master's degree in Computer Science, Engineering, Information Systems, or related field and 3+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.

PhD in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.

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