{
    "schema_version": "via_ai_product_manager_jobs_v1",
    "data_type": "ai_product_manager_jobs",
    "job": {
        "job_title": "Senior Researcher, Natural Language Processing",
        "job_level": "高级",AI生成
        "work_category": "全职",AI生成
        "department": "IEG",
        "location": "伦敦",
        "work_mode": "未披露",AI生成
        "job_category": "算法/研究"AI生成
    },
    "company": {
        "company_name": "腾讯",
        "company_industry": "互联网 / 科技 / 人工智能",
        "company_size": "10000人以上",
        "company_website": "tencent.com",
        "company_description": "腾讯是一家互联网和科技公司，业务覆盖通信与社交、数字内容、游戏、金融科技、企业服务、云计算与人工智能等方向。"
    },
    "compensation": {
        "salary_range": {
            "min": "",
            "max": "",
            "currency": "未披露",
            "cycle": "未披露"
        },
        "benefits": "未披露"
    },
    "requirements": {
        "working_years": "",
        "degree": "未披露",
        "language": "未披露",AI生成
        "visa_support": "未披露",
        "responsibilities": "1. Responsible for NLP research and development including LLM reasoning, conversational agents, machine translation, part-of-speech tagging, sentence analysis and named entity recognition.\n2. Optimize existing online algorithms, develop efficient and reliable NLP solutions by combining business needs and data.\n3. Explore applications of NLP techniques and deep learning algorithms in games.\n4. Follow the latest developments in academia and industry and quickly apply findings in your work.\r\nWork Location: UK-London",
        "requirements_text": "1. Familiar with NLP fundamentals, with a strong understanding of statistical models, related machine learning principles, and experience working on NLP related projects.\n2. Proficient in at least one programming language, familiar with basic data structures and algorithms.\n3. Practical experience working with large language models (e.g., DeepSeel, Qwen, LLaMA), including model fine-tuning, instruction tuning, and evaluation. Familiarity with training optimization frameworks like DeepSpeed, FSDP, and LoRA is highly preferred.\n4. Experience using tools and frameworks such as LLaMAFactory, Hugging Face Transformers, LangChain, or OpenLLM for model deployment, serving, or pipeline development. Comfortable with using modern MLOps practices for NLP workflows.\n5. Demonstrated ability to publish research results in top-tier NLP conferences (e.g., ACL, EMNLP, NAACL, NeurIPS, ICLR) or practical contributions to open-source LLM/NLP projects is preferred.\n6. Comfortable with both classical (e.g., CRF, SVM) and deep learning approaches (e.g., Transformers, RNNs, CNNs, LSTMs, GANs). Deep understanding of transformer architectures, attention mechanisms, and distributed training techniques is a strong advantage.\n\n#LI-RL1",
        "skills": [AI生成
            "机器学习",
            "深度学习",
            "自然语言处理",
            "大语言模型"
        ],
        "preferred_qualifications": "3. Practical experience working with large language models (e.g., DeepSeel, Qwen, LLaMA), including model fine-tuning, instruction tuning, and evaluation. Familiarity with training optimization frameworks like DeepSpeed, FSDP, and LoRA is highly preferred.；5. Demonstrated ability to publish research results in top-tier NLP conferences (e.g., ACL, EMNLP, NAACL, NeurIPS, ICLR) or practical contributions to open-source LLM/NLP projects is preferred.",
        "job_highlights": "未披露"
    },
    "contact": {
        "recruiter_name": "未披露",
        "contact_methods": [
            {
                "type": "apply_link",
                "value": "https://tencent.wd1.myworkdayjobs.com/Tencent_Careers/job/UK-London/Senior-Researcher--Natural-Language-Processing_R105817-1"
            }
        ]
    },
    "posting": {
        "posting_id": "1947255265967603712",
        "post_date": "2025-07-21",
        "hire_time": "未披露",
        "update_date": "20260917",
        "job_url": "https://tencent.wd1.myworkdayjobs.com/Tencent_Careers/job/UK-London/Senior-Researcher--Natural-Language-Processing_R105817-1"
    },
    "fulltext": "Senior Researcher, Natural Language Processing\n1. Responsible for NLP research and development including LLM reasoning, conversational agents, machine translation, part-of-speech tagging, sentence analysis and named entity recognition.\n2. Optimize existing online algorithms, develop efficient and reliable NLP solutions by combining business needs and data.\n3. Explore applications of NLP techniques and deep learning algorithms in games.\n4. Follow the latest developments in academia and industry and quickly apply findings in your work.\r\nWork Location: UK-London\n1. Familiar with NLP fundamentals, with a strong understanding of statistical models, related machine learning principles, and experience working on NLP related projects.\n2. Proficient in at least one programming language, familiar with basic data structures and algorithms.\n3. Practical experience working with large language models (e.g., DeepSeel, Qwen, LLaMA), including model fine-tuning, instruction tuning, and evaluation. Familiarity with training optimization frameworks like DeepSpeed, FSDP, and LoRA is highly preferred.\n4. Experience using tools and frameworks such as LLaMAFactory, Hugging Face Transformers, LangChain, or OpenLLM for model deployment, serving, or pipeline development. Comfortable with using modern MLOps practices for NLP workflows.\n5. Demonstrated ability to publish research results in top-tier NLP conferences (e.g., ACL, EMNLP, NAACL, NeurIPS, ICLR) or practical contributions to open-source LLM/NLP projects is preferred.\n6. Comfortable with both classical (e.g., CRF, SVM) and deep learning approaches (e.g., Transformers, RNNs, CNNs, LSTMs, GANs). Deep understanding of transformer architectures, attention mechanisms, and distributed training techniques is a strong advantage.\n\n#LI-RL1",
    "standard_classification": {
        "std_category_code": "T036",
        "std_aux_codes": [],
        "std_category_status": null,
        "std_taxonomy_version": "20261104"
    }
}