1 day ago
Ho Chi Minh City, VietnamStaff+
Responsibilities
- Train and fine-tune large-scale LLMs and VLMs for multimodal and agentic tasks.
- Build end-to-end agentic AI prototypes for on-device and cloud deployment.
- Improve model efficiency through quantization, pruning, and distillation.
- Implement experiments in Python and PyTorch and manage benchmarks across large-scale distributed environments.
- Collaborate with hardware, software, and systems teams to bring research into products.
- Share knowledge, mentor junior engineers, and support cross-functional research initiatives.
Requirements
- Bachelor's, master's, or PhD in Computer Science, Machine Learning, Engineering, Information Systems, or a related field.
- At least 3 years of hands-on experience designing, training, or optimizing LLMs, VLMs, or foundation models in real-world settings.
- At least 2 years of related engineering experience with a PhD, 3 years with a master's degree, or 4 years with a bachelor's degree under the stated minimum qualifications.
- Strong knowledge of generative AI, multimodal architectures, and large-scale training strategies.
- Proficiency in Python and PyTorch, with experience in distributed training frameworks such as DeepSpeed or FSDP.
- Experience applying quantization, pruning, or distillation to reduce model footprint without degrading quality.
- Preferred qualifications include a PhD, hardware-aware AI optimization, edge-device deployment, publications at venues such as NeurIPS, ICML, CVPR, or ACL, and experience mentoring engineers or leading cross-functional research initiatives.
Categories
AI ResearchML Engineering
About Qualcomm
Qualcomm is a public semiconductor company headquartered in San Diego, founded in 1985, that designs and sells wireless chipsets and platforms for mobile devices, automotive, IoT, and networking, notably the Snapdragon application processors and 5G modems. It also licenses a large portfolio of cellular patents to device makers, generating revenue alongside chip sales; its technology underpins many Android smartphones and emerging automotive and edge-compute systems, and it trades on NASDAQ as QCOM.