Generative AI Algorithms Engineer at Qualcomm Technologies Inc Turkey

Beijing, Beijing, China -

Full Time

Start Date

Immediate

Expiry Date

07 Jan, 26

Salary

0.0

Posted On

09 Oct, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, PyTorch, Post-Training Quantization, Quantization-Aware Training, LoRA, Model Compression, Reinforcement Learning, On-Device Learning, Federated Learning, Continual Learning, Multimodal Inference, Efficient Inference, Diffusion-Based Methods, Model Efficiency, AI Coding Assistants

Industry

Telecommunications

Description

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. 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. Qualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail disability-accomodations@qualcomm.com or call Qualcomm's toll-free number found here. Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities. (Keep in mind that this email address is used to provide reasonable accommodations for individuals with disabilities. We will not respond here to requests for updates on applications or resume inquiries). Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law. To all Staffing and Recruiting Agencies: Our Careers Site is only for individuals seeking a job at Qualcomm. Staffing and recruiting agencies and individuals being represented by an agency are not authorized to use this site or to submit profiles, applications or resumes, and any such submissions will be considered unsolicited. Qualcomm does not accept unsolicited resumes or applications from agencies. Please do not forward resumes to our jobs alias, Qualcomm employees or any other company location. Qualcomm is not responsible for any fees related to unsolicited resumes/applications. If you would like more information about this role, please contact Qualcomm Careers.

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Responsibilities

Lead or contribute to the end-to-end training, fine-tuning, and quantization of various models, especially in low-bit quantization. Design and implement scalable systems for model training and deployment, while conducting research in generative AI and efficient computation methods.