Company:

Qualcomm Korea YH

Job Area:

Engineering Group, Engineering Group > Machine Learning Engineering

General Summary:

Job Summary

This role is responsible for researching and developing immediate and rapid end-to-end integration of advanced AI technologies into functional on-device systems. Unlike roles focused primarily on component-wise research or isolated engineering tasks, this position prioritizes building a cohesive, working system. You will harmonize the entire pipeline—from multimodal data acquisition and processing to the seamless orchestration of agentic LLM and LVM (VLM) modules. While our primary mission is fast-paced integration, you will also lead additional research goals such as on-device privacy and individual feature enablement as the system scales.

Key Responsibilities

• System Design & End-to-End Integration: Work on the holistic integration of the full agentic pipeline, encompassing multimodal data acquisition, pre-processing, and the unified combination of agentic LLM and LVM/VLM modules.
• Fast Prototyping: Proactively identify, implement, and evaluate a wide range of baseline models and state-of-the-art methodologies to establish the optimal architecture for edge environments.
• System-Level Optimization: Ensure the immediate execution and high performance of integrated models under on-device hardware constraints (latency, power, memory).
• Applied Research on Privacy & Efficiency: Validate the feasibility of specific agentic features and conduct applied research on privacy-preserving techniques within on-device agentic workflows while maximizing system efficiency.
• Use Case Discovery: Actively identify and build high-fidelity PoCs (Proof of Concepts) that demonstrate the practical value of an integrated agentic AI within the mobile and embedded ecosystem.
  
  
  

Qualifications

• Ph.D. or Master’s degree in Computer Science, AI, or a related field.
• Research Background: Practical research and development experience to navigate and understand the latest AI advancements from academic and industrial research is required to keep our systems at the cutting edge.
• Expert Implementation Skills: Mastery of Python and extensive experience with ML frameworks like PyTorch for end-to-end system development and deployment.
• Technical Breadth: Strong practical understanding of the interaction between Language Models (LLM/SLM) and Vision-Language Models (VLM) within an agentic framework.
• System-Level Thinking: Proven ability to transition from reading research papers to implementing and benchmarking complex AI algorithms.
  
  
  

Preferred Qualifications

• Full-Stack AI Integration: Proven track record of building and benchmarking comprehensive AI pipelines (e.g., combining real-time data ingestion with multimodal reasoning).
• On-Device Expertise: Experience in software development for mobile or embedded environments, including optimization for NPUs, DSPs, or mobile GPUs.
• Agile Prototyping: A history of quickly validating new AI-driven service scenarios through rapid iteration and PoC development.
• System-Level Privacy: Knowledge of on-device privacy techniques and their practical implementation within an integrated AI workflow.
• Academic Research: Solid publication record in academic venues such as NeurIPS, CVPR, ACL (main), and also NAACL, WACV, COLM, etc.,
  
  
  

Minimum Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field with 2+ years of Hardware/Software Engineering or related work experience.
• OR Master's degree in a related field with 1+ year of related work experience.
• OR PhD in a relevant field.
  
  
  

Minimum Qualifications:

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

OR

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

OR

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

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