Senior On-Device ML Research Engineer

QUALCOMM, Inc.; 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 high-caliber team optimizing machine-learning solutions for power, memory, and compute efficiency.

Our team spans software, hardware, and systems and impacts products across mobile, compute, automotive, cloud, and IoT. You will work on model efficiency, on-device learning, and advanced ML compute paradigms, contributing to state-of-the-art research and deployment-ready methods for resource-constrained

Qualifications

• PhD in AI, CS, Engineering, Information Systems or related field, or MS with 4+ years of ML research.
• Strong programming in Python and PyTorch; experience building training and evaluation pipelines.
• Exceptional analytical, development, debugging and problem solving skills.

Responsibilities

• Drives systems innovations for model efficiency advancement on device and cloud, balancing memory, speed and power.
• Conducts fundamental machine learning research to create new models or training methods (LLMs, transformers, diffusion models, etc.).
• Performs advanced platform research to enable new ML compute paradigms (edge-cloud, on-device learning, federated learning, quantum ML).
• Creates new ML models for use cases including computer vision, audio, NLP, power management, wireless, graphics, and chip design.
• Design, develop and test software for ML frameworks to run efficiently on edge devices.
• Research, design, develop, enhance, and implement components of ML compilers for HW Accelerators.
• Design, implement and train DL/RL algorithms in high-level languages/frameworks.

Skills

Python

PyTorch

Deep learning

Research

Education

PhD in AI/CS/Eng/IS or MS + 4+ yrs ML research

MS + 1+ yr ML/related work

Bachelors in CS/Engineering/IS or related

Tools

Python tooling

Job description

Qualcomm Technologies, Inc. is seeking world-class researchers in deep learning to join a high-caliber team optimizing machine-learning solutions for power, memory, and compute efficiency.

Our team spans software, hardware, and systems and impacts products across mobile, compute, automotive, cloud, and IoT. You will work on model efficiency, on-device learning, and advanced ML compute paradigms, contributing to state-of-the-art research and deployment-ready methods for resource-constrained