Algorithm Researcher — Agentic Vision

Business Unit

Technology Engineering Group (TEG) is responsible for supporting the company and its business groups on technology and operational platforms, as well as the construction and operation of R&D management and data centers, TEG provides users with a full range of customer services. As the operator of the largest networking, devices, and data center in Asia,TEG also leads the Tencent Technology Committee in strengthening infrastructure R&D through internal and distributed open source collaboration, constructing new platforms and supporting business innovation.

What the Role Entails

- Lead Post-Training R&D System:​ Take ownership of the full-stack algorithm R&D for post-training of Vision-Language Models (VLMs). Deeply explore Supervised Fine-Tuning (SFT) and reinforcement learning alignment algorithms (e.g., RLHF, PPO, GRPO), continuously pushing the boundaries and elevating the capability ceiling of vision foundation models.

- Advance Visual Foundation Perception:​ Dedicate to optimizing the model's low-level visual perception, fine-grained understanding, and spatial awareness. Proactively explore Visual Agent solutions including "thinking with images," endowing models with deeper comprehension of and execution capability in the physical world.

- Enhance Large Model Reliability:​ Strive to substantially reduce hallucinations in multimodal large models. Investigate the root causes of hallucination, build comprehensive hallucination detection pipelines, implement refined data cleaning strategies, and spearhead the R&D of reinforcement learning-based hallucination removal solutions.

- Empower User Interaction Scenarios:​ Align and optimize models for real-world user scenarios, strengthening the model's logical reasoning, instruction following, and detail perception capabilities in multi-turn image-text interactions. Build an online badcase monitoring and data flywheel feedback loop to continuously refine and deliver exceptional interaction experiences.

Who We Look For

- Master's degree or above in Computer Science, Software Engineering, Artificial Intelligence, or related fields.

- Deep understanding of the Transformer architecture and large language model training principles. Extensive research and hands-on experience in at least one post-training domain, including LLM Alignment, RLHF, Reward Modeling, Personalized LLM, and Memory/RAG mechanisms.

- Rich practical experience in user profiling, recommendation systems integrated with large models, or long-context/long-term memory model optimization.

- Solid algorithm foundation and engineering implementation skills, proficient in Python, and familiar with deep learning frameworks such as PyTorch and TensorFlow.

- Hands-on experience with distributed training, familiar with large-scale training and inference frameworks including Megatron-LM, DeepSpeed, and vLLM.

- Experience in training or fine-tuning models at the tens-of-billions to hundreds-of-billions parameter scale.

- Excellent research acumen, with publications at top-tier conferences (NeurIPS, ICLR, ICML, ACL, EMNLP, etc.) or significant contributions to high-impact open-source projects (e.g., on HuggingFace).

- Strong technical passion and self-drive, adept at analyzing and solving complex problems, with good teamwork and communication skills.

Equal Employment Opportunity at Tencent

As an equal opportunity employer, we firmly believe that diverse voices fuel our innovation and allow us to better serve our users and the community. We foster an environment where every employee of Tencent feels supported and inspired to achieve individual and common goals.