{"id":777502,"url":"https://alion.io/job/qualcomm-intern-generative-ai-model-personalization-and-efficient-fine-tuning","title":"Intern - Generative AI model personalization and efficient fine-tuning","company":{"id":94,"name":"Qualcomm","domain":"qualcomm.com","url":"https://alion.io/company/qualcomm","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Eightfold","truth_index":{"grade":"B","score":70,"open_postings":1118,"ghost_share":0.244,"stale_share":0.135,"repost_share":0.462,"time_to_fill_p50_days":74,"computed_at":"2026-10-25T05:49:26Z"}},"role":"Early Careers","role_family":"Early Careers","seniority":"intern","employment_type":"internship","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Seoul, South Korea"],"countries":["KR"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":36000,"max_usd":71000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":768},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Few-Shot Learning","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"LLM","optional":false},{"name":"LoRA","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Distillation","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reward Modeling","optional":false},{"name":"RLHF","optional":false},{"name":"Self-Supervised Learning","optional":false},{"name":"TensorFlow","optional":false},{"name":"Vision-Language-Action","optional":false},{"name":"PEFT","optional":true},{"name":"Rest-Assured","optional":true}],"status":"live","first_seen_at":"2026-03-31T00:00:00Z","employer_posted_date":"2026-07-15","last_verified_at":"2026-10-25T10:43:32Z","board_verified":true,"closed_at":null,"days_open":208,"trust":{"level":"ok","repost_count":1,"flags":[],"days_open":208},"description":"Company:\nQualcomm Korea YHJob Area:\nInterns Group, Interns Group > Interim Engineering Intern - SWQualcomm Overview:\nQualcomm is a company of inventors that unlocked 5G ushering in an age of rapid acceleration in connectivity and new possibilities that will transform industries, create jobs, and enrich lives. But this is just the beginning. It takes inventive minds with diverse skills, backgrounds, and cultures to transform 5Gs potential into world-changing technologies and products. This is the Invention Age - and this is where you come in.\nGeneral Summary:\nWe are making AI technology be around us at edge devices, attend and assist people and learn from interaction without many labels, together with other devices in a federated way. We are based in Seoul and work together with our other team members in San Diego, Amsterdam, and Beijing.\nWe are looking for research interns interested in developing new theories and algorithms in the following areas, with a strong focus on Generative AI Models and its personalization:\nGenerative foundation models : LLM, LVM, LMM, VLA, etc.\nPersonalization and on-device adaptation of generative models\nEfficient fine-tuning techniques (LoRA, adapters, parameter-efficient tuning)\nMulti-modal generation (text-to-image, text-to-video, audio-visual)\nDiffusion / transformer models and their optimization\nKnowledge distillation and compression for generative models\nQuantization and low-bit processing for LLM/LMM\nReinforcement Learning for Generative AI (RLHF, policy optimization, reward modeling)\nUnsupervised / semi-supervised / self-supervised learning\nMeta-Learning / few-shot learning / domain adaptation / knowledge transfer\nThe developed technologies can be deployed on mobile device applications and have a worldwide impact on on-device learning algorithms using Qualcomm chips. Also, the research results are expected to be published in top-tier conferences and journals.\nMinimum Qualifications\nCurrently pursuing a Ph.D. or Master's degree in Electrical Engineering, Computer Science, Artificial Intelligence, or a related field.\nStrong knowledge of machine learning and deep learning\nResearch experience in Generative AI (LLM, LVM, LMM, VLA) and/or personalization techniques\nExperience implementing ML algorithms using Python and deep learning frameworks (e.g., PyTorch, TensorFlow)\nStrong research and problem-solving skills demonstrated through publications or research projects.\nPublication requirement:At least 2 first-author papers, including at least 1 paper in a top-tier conference (e.g., NeurIPS, CVPR, ICML, ICCV, ICLR, ECCV, ACL)\n\nPreferred Qualifications\nExtensive and diverse experience with generative AI models, including LLMs, LVMs, LMMs, and multi-modal architectures\nFamiliarity with efficient adaptation techniques such as LoRA, adapters, and other parameter-efficient fine-tuning methods\nHands-on experience with model optimization for edge deployment, including quantization, pruning, and low-bit inference/training\nTrack record of publications at leading AI conferences (e.g., NeurIPS, CVPR, ICML, ICCV, ICLR, ECCV, ACL)\nApplicants: 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  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.\nQualcomm 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.\nTo 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. 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