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Salary
≈ $125k – $222k per year (Estimated)
Location
In office (London)
Seniority
Senior

Confirmed on the employer's own hiring board on Nov 3, 2026. First seen by Alion on Jul 21, 2025. Tencent scores B on the Alion truth index.

Overview
Company
Impact
Profile match
Tencent is a Chinese technology conglomerate founded in Shenzhen in 1998 and one of the largest internet companies in the world by revenue. Its WeChat super-app combines messaging, social feeds, payments, mini-programs and official accounts into the default digital layer of Chinese daily life, with well over a billion monthly users. The group is also the biggest video game company on the planet through domestic publishing and stakes in Riot, Supercell and Epic Games, and runs Tencent Cloud, Tencent Music, a large fintech business and the Hunyuan family of foundation models.
Backed by ValueAdd VC

岗位职责 / 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.

2. Optimize existing online algorithms, develop efficient and reliable NLP solutions by combining business needs and data.

3. Explore applications of NLP techniques and deep learning algorithms in games.

4. Follow the latest developments in academia and industry and quickly apply findings in your work.

Work Location: UK-London

岗位要求 / Requirements

1. Familiar with NLP fundamentals, with a strong understanding of statistical models, related machine learning principles, and experience working on NLP related projects.

2. Proficient in at least one programming language, familiar with basic data structures and algorithms.

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.

4. 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.

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.

6. 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.

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