Senior ML Research Engineer - Edge & On-Device AI

Qualcomm Incorporated; San Diego (CA); On-site; USD 141,000 - 211,000; Full time

Job summary

Qualcomm Technologies, Inc. is seeking world-class researchers in deep learning to join a premier team that invents ML technologies for power, memory, and speed efficiency.

You will collaborate across software, hardware and systems to deploy state-of-the-art methods worldwide. The role spans applied and fundamental ML research in areas such as efficient models, large language models, diffusion models, and federated learning, with opportunities to publish and influence real-world AI deployments

Qualifications

• Bachelor's level or higher with relevant degree and research exposure.
• Strong background in computer science and deep learning basics as foundational knowledge.
• Proficiency in Python and PyTorch; ability to design and run complex training/evaluation pipelines.

Responsibilities

• Drives model efficiency on device and cloud, including compression, sparsity, quantization and end-to-end optimization.
• Conducts fundamental ML research to create new models or training methods (LLMs, transformers, diffusion models, RL).
• Explores compute paradigms like on-device learning, edge-cloud federated learning, and quantum ML techniques.
• Develops ML models for computer vision, NLP, audio, video, and related domains for resource-constrained devices.
• Designs, develops, tests and optimizes software for ML frameworks to run efficiently on edge devices.
• Researches and builds components of ML compilers for HW accelerators.
• Designs and trains DL/RL algorithms in high-level languages/frameworks.

Skills

Python

PyTorch

Deep learning basics

Model optimization

Education

PhD in AI/CS/Engineering or related

MS in AI/CS/Engineering or related with 4+ years ML research

Tools

ML accelerators

Job description

Qualcomm Technologies, Inc. is seeking world-class researchers in deep learning to join a premier team that invents ML technologies for power, memory, and speed efficiency.

You will collaborate across software, hardware and systems to deploy state-of-the-art methods worldwide. The role spans applied and fundamental ML research in areas such as efficient models, large language models, diffusion models, and federated learning, with opportunities to publish and influence real-world AI deployments