AI/ML Algorithms Engineer

AI/ML Algorithms Engineer at Qualcomm

Qualcomm AI Research is actively seeking world-class algorithms engineers specializing in general domain machine learning, particularly deep learning, generative AI, large language models (LLM), and large vision models (LVM). Join a high-caliber team dedicated to building advanced machine learning technology, delivering best-in-class solutions, and developing user-friendly model optimization tools like Qualcomm Innovation Center’s AI Model Efficiency Toolkit (AIMET). This role enables state-of-the-art networks to run efficiently on devices with limited power, memory, and computation.

Members of our team have the unique opportunity to engage in cutting-edge research while simultaneously contributing technology that will be deployed globally in our industry-leading devices. You will be an integral part of a multi-disciplinary, talented team focused on optimizing generative AI for on-device applications, collaborating in a cross-functional environment spanning hardware, software, and systems. See your designs brought to life on industry-leading chips embedded in the next generation of smartphones, autonomous vehicles, robotics, and IoT devices.

Key Responsibilities

• Algorithms research and development in the area of LLM, LVM, and other cutting-edge generative model architectures.
• Advanced quantization algorithms research and development for complex generative models, including (but not limited to) FlexRound, PrefixQuant, GPTQ, SpinQuant, adaRound, CLE, FPTQuant, UPQ, Automatic mixed precision, and other INT/FP Quant algorithms.
• Experiments and research on LLM training and fine-tuning, including pre-training, supervised fine-tuning (SFT), reinforcement learning (RL), 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, and speculative decoding.
• Model compression, whether lossy or lossless, structural, and neural search.
• Optimization-based learning and learning-based optimization.
• Generative AI Systems prototyping to apply solutions toward systems innovations for model efficiency advancement on device as well as in the cloud.
• Python and PyTorch programming.

Qualifications

Candidates should possess a major in Computer Science, Computer Engineering, Information Systems, Electrical Engineering, or a related field. A Master's degree is preferred, with a PhD being highly valued.

Minimum Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field with 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
• OR Master's degree in Computer Science, Engineering, Information Systems, or a related field with 1+ year of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
• OR PhD in Computer Science, Engineering, Information Systems, or a related field.

Key Skills/Competency

• Machine Learning
• Deep Learning
• Generative AI
• LLM/LVM Architectures
• Quantization Algorithms
• Model Compression
• Efficient Inference
• PyTorch
• Python Programming
• Cross-functional Collaboration