Generative AI Algorithms Engineer
Job Description
- Hands-on experience with training or quantization pipelines such as Llama Factory or AIMET.
- Experience in LoRA adapter-tuning, speculative decoding, model compression.
- Experience in developing or optimizing memory-efficient, high-speed inference engines such as vLLM and SGLANG.
- Knowledge in state-of-the-art PTQ and QAT algorithms.
- Knowledge in reinforcement learning (RL).
- Knowledge in on-device learning, federated learning, or continual learning.
- Experience using AI coding assistants such as Claude code, Codex, or Cursor is a plus.
- Master’s or PhD in Computer Science, Electrical Engineering, AI, or a related technical field.
- Experience in training, fine-tuning, and quantization of LLM/LVM/LMM models, especially low-bit quantization.
- Ability to design and implement scalable, robust systems and engineering pipelines for model training, evaluation, quantization (PTQ and QAT), and on-device deployment.
- Algorithms research and development in VLM, VLA, and other multimodality models, diffusion-based methods for image and text generation, efficient computation (MoE, LoRA or others).
- Experience of multimodal inference and training, such as image generation, 3D, video generation, editing, ViT and other models.
- Efficient inference algorithms research and advanced quantization, e.g. batching, KV caching, efficient attentions, long context, speculative decoding, GPTQ, SpinQuant, automatic mixed precision.
- Solid programming skills in Python, with proficiency in PyTorch.
- Demonstrated experience in both PTQ (Post-Training Quantization) and QAT (Quantization-Aware Training) for deep neural networks, especially under low-bit (≤8 bits) regimes.
- World-class health benefit options providing world-class coverage to employees and their eligible dependents.
- Programs designed to help employees build and prepare for a financially secure future.
- Self and family resources to build emotional/mental strength and resilience, as well as define purpose.
- Wellbeing programs and resources to help employees Live+Well and Work+Well.
- Continuous learning and development programs.
- Tuition reimbursement.
- Mentorships.
Job Posting Date
2025-10-09
Additional Job Posting Location
Shanghai CHN
Company:
Qualcomm China
Job Area:
Engineering Group, Engineering Group > Machine Learning Engineering
General Summary:
About us:
We are Qualcomm AI Research that are advancing AI to make its core capabilities – perception, reasoning, and action – ubiquitous across devices. Our mission is to make breakthroughs in fundamental AI research and scale them across industries. By bringing together some of the best minds in the field, we're pushing the boundaries of what's possible and shaping the future of AI. Welcome to visit our website at AI Research Areas | Intelligence on Devices | Qualcomm
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Job area: Engineering – Machine Learning Engineering
Minimum Qualifications
- Master’s or PhD in Computer Science, Electrical Engineering, AI, or a related technical field.
Key Responsibilities
- Lead or contribute to the end-to-end training, fine-tuning, and quantization of LLM/LVM/LMM models, especially in low-bit quantization
- Design and implement scalable, robust systems and engineering pipelines for model training, evaluation, quantization (PTQ and QAT), and can support customers' on-device deployment.
- Algorithms research and development in VLM, VLA and other multimodality models, diffusion-based methods for image and text generation, efficient computation (MoE, LoRA or others).
- Experience of multimodal inference and training, such as image generation, 3D, video generation, editing, ViT and other models.
- Efficient inference algorithms research and advanced quantization, e.g. batching, KV caching, efficient attentions, long context, speculative decoding, GPTQ, SpinQuant, automatic mixed precision.
- Apply solutions toward systems innovations for model efficiency advancement on device as well as in the cloud.
- Research and integrate state-of-the-art algorithms in generative AI, quantization techniques, knowledge distillation, model compression, and efficient inference.
- Build, maintain, and automate test suites and profiling/debugging tools to validate and benchmark model performance and deployment effectiveness.
- Document methodologies and results, and present key findings to stakeholders.
Preferred Skills and Experience:
- Solid programming skills in Python, with proficiency in PyTorch;
- Demonstrated experience in both PTQ (Post-Training Quantization) and QAT (Quantization-Aware Training) for deep neural networks, especially under low-bit (≤8 bits) regimes.
- Hands-on experience with training or quantization pipelines such as Llama Factory or AIMET.
- Experience in LoRA adapter-tuning, speculative decoding, model compression.
- Experience in developing or optimizing memory-efficient, high-speed inference engines such as vLLM and SGLANG.
- Knowledge in start-of-the-art PTQ and QAT algorithms
- Knowledge in reinforcement learning (RL)
- Knowledge in on-device learning, federated learning, or continual learning
- Experience using AI coding assistants such as Claude code, Codex, or Cursor is a plus
Minimum Qualifications:
• Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 4+ 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 3+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
OR
PhD in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
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