Machine Learning Engineer (GenAI Benchmarking)

Company

Qualcomm Korea YH

Job Area

Engineering Group, Engineering Group > Machine Learning Engineering

General Summary

Job Overview

As a leading technology innovator, Qualcomm pushes the boundaries of what's possible to enable next-generation experiences and drives digital transformation to help create a smarter, connected future for all. As a Qualcomm Machine Learning Engineer, you will create and implement machine learning techniques, frameworks, and tools that enable the efficient discovery and utilization of state-of-the-art machine learning solutions across mobile, edge, auto, and IoT platforms.

This role offers hands-on experience with the latest generative AI models and benchmarking tools, deepens your understanding of model optimization and deployment strategies, and helps define best practices for evaluating next-generation AI solutions.

Responsibilities

As a GenAI Benchmarking Engineer at Qualcomm, you will play a pivotal role in shaping the future of on-device AI by designing and implementing comprehensive benchmarking frameworks for advanced machine learning models. Rather than directly evaluating and verifying models at every deployment stage, from initial simulation to fully deployed on-device solutions, your primary responsibility will be to develop, automate, and maintain robust frameworks that enable other engineers and developers to efficiently assess and validate models throughout the entire deployment pipeline. This ensures that all stakeholders have reliable tools and methodologies to uphold the highest standards of quality, efficiency, and robustness.

You may contribute to one or more of the following areas, depending on project needs and your expertise:

•      Design, implement, and automate end-to-end benchmarking and verification frameworks to support evaluation of models and features throughout all deployment stages, from simulation to on-device execution.
•      Analyze state-of-the-art GenAI models and benchmarks, and provide guidance on selecting and applying appropriate evaluation methods for diverse use cases.
•      Develop robust and efficient benchmarking methodologies to assess model performance, including the impact of compression and acceleration technologies.
•      Collaborate with software and system engineers to understand requirements, plan evaluation strategies, and define key performance metrics (KPIs).
  
  
  

Required Skills

•      Proficiency in Python and ML frameworks such as PyTorch or TensorFlow , with hands-on experience implementing and modifying model architectures.
•      Experience working with neural network architectures such as LLMs, LVMs, LMMs, and understanding their behavior during generation.
•      Solid software development skills, including debugging, profiling, and performance tuning.
  
  
  

Preferred Skills

•      2+ years of proven experience in software development for machine learning with strong programming skills in Python and software design.
•      Experience in benchmarking generative models, including evaluation of output quality, diversity, and latency.
•      Familiarity with CI/CD workflows and automation tools for benchmarking and model evaluation.
•      Familiarity with AI agent frameworks (like LangChain, LlamaIndex, Autogen).
•      Experience in designing generalized AI solutions such as Retrieval-Augmented Generation (RAG) systems.
•      Experience using/integrating Qualcomm AI Stack products (e.g. QNN, SNPE, QAIRT).
•      Experience optimizing models for hardware acceleration, including quantization and deployment on embedded systems (Android/Linux).
  
  
  

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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