AI​/Machine Learning Researcher AI Research

Company:

Qualcomm Technologies, Inc.

Engineering Group, Engineering Group >
Machine Learning Researcher

Qualcomm AI Research is looking for world‑class researchers in artificial intelligence to join our team. Members of our global team form a multi‑disciplinary core research group within Qualcomm which spans software, hardware, and systems, and we contribute technology deployed worldwide by partnering with our business teams across mobile, compute, automotive, cloud, and IOT. We also perform and publish state‑of‑the‑art research on a wide range of topics in artificial intelligence, ranging from general theory to techniques that enable deployment on resource‑constrained devices.

We are dedicated to pushing the boundaries of AI across Generative AI, LLM and Multi‑Modal Foundation Models, Reasoning, Reinforcement Learning, Agentic AI, and Autonomy, with a strong emphasis on efficient on‑device intelligence. Please see our AI focus page at  for more details.

Ideal candidates will have a strong publication record in top‑tier AI/ML conferences and deep expertise in generative AI, including Multi‑Modal Foundation Models, Efficient Architectures, LLM Reasoning, Reinforcement Learning, Agentic AI, and Autonomy. You will lead and contribute to high‑impact research initiatives, collaborate with global teams, and help transition cutting‑edge models into real‑world applications. Senior‑level candidates are expected to demonstrate research leadership, mentor junior researchers and interns, and drive innovation aligned with Qualcomm’s strategic priorities.

• Conduct cutting‑edge research in efficient generative AI: LLMs and Multi‑Modal Foundation Models, LLM Reasoning, Reinforcement Learning, Agentic AI and others.
• Provide technical leadership in research and applied projects and guide research directions to best support company objectives.
• Mentor interns, junior researchers and engineers, fostering collaboration, growth and excellence in a dynamic R&D environment.
• Lead efforts in transitioning research into production‑ready solutions, enabling real‑world applications and commercial impact.
• Develop creative solutions with consideration of practical challenges on devices.
• Implementation and evaluation of possible solutions in both simulation and on‑device environments.
• Contribute to Qualcomm’s strategic initiatives in efficient AI and embedded intelligence.
• Publish in top‑tier conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ACL, EMNLP).

• Proven research excellence demonstrated by publications in top‑tier AI/ML conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ACL, EMNLP).
• Deep expertise in Generative AI, LLMs, Multi‑Modal Foundation models, LLM Reasoning, Reinforcement Learning, and Agentic AI.
• Hands‑on experience with model development pipelines, including training, fine‑tuning, evaluation, and optimization.

• PhD with 4+ years in Computer Science, Electrical Engineering, or related fields or MS with 8+ years of AI research, or related work experience.
• Experience in LLM efficiency research such as efficient attention, inference acceleration, or KV cache compression.
• Experience in on‑device AI deployment on mobile or edge devices.
• Familiar with model optimization techniques such as quantization and distillation.

• Be part of a globally recognized AI research organization.
• Work on impactful projects with real‑world deployment.
• Collaborate with top researchers across Qualcomm’s global network.
• Thrive in an inclusive, innovation‑driven environment.
• Enjoy competitive compensation and career development opportunities.

• Master’s degree in Computer Engineering, Computer Science, Electrical Engineering, or related field and 4+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
• PhD in Computer Engineering, Computer Science, Electrical Engineering, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
• 6+ months of experience developing and/or optimizing machine learning models,…