Generative AI Algorithms Engineer - Machine Learning Engineering

Company:

Job Area:

Engineering Group, Engineering Group > Machine Learning Engineering

General Summary:

Qualcomm AI Research is looking for world-class algorithms engineers in general domain machine learning, especially generative AI, LLM, LVM, VLM and omni models. Come join a high-caliber team of engineers building advanced artificial intelligence technology, best-in-class solutions, and user friendly model optimization tools such as  Qualcomm Innovation Center’s AI Model Efficiency Toolkit (https://github.com/quic/aimet) to enable state-of-the-art networks to run on devices with limited power, memory, and computation. Members of our team enjoy the opportunity to participate in cutting edge research while simultaneously contributing technology that will be deployed worldwide in our industry-leading devices. You will be part of a multi-disciplinary talented team working on on device generative AI optimization. Collaborate in a cross-functional environment spanning hardware, software and systems. See your design in action on industry-leading chips embedded in the next generation of smartphones, autonomous vehicles, robotics, and IOT devices.

The R&D work responsibility for this position focuses on the following

• Algorithms research and development in the area of LLM, LVM, VLM, omni models and other cutting‑edge generative model architectures.  
• Advanced architecture research and development for complex generative models, including (but not limited to) MoE, such as expert routing, load balancing, sparse activation, expert parallelism and others
• Advanced quantization algorithms research and development for complex generative models, including (but not limited to) FlexRound, PrefixQuant, GPTQ, SpinQuant, Automatic mixed precision, and other INT/ FP Quant algorithms.
• Experiments and research on generative models training and fine‑tuning, including pre‑training, SFT, PEFT, RL, reasoning, loss‑function design and optimization, as well as quantization‑aware training built on top of advanced quantization methods.
• Efficient inference algorithms research and development, e.g. batching, KV caching, efficient attentions, long context, speculative decoding.
• Model compression, lossy or lossless, structural and neural search.
• Optimization based learning and learning based optimization.
• Generative AI Systems prototyping
• Apply solutions toward systems innovations for model efficiency advancement on device as well as in the cloud.
• Python, Pytorch programmer

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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