Algorithm Researcher — Agentic Vision

Job Description
- 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.

Skills Required
-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 inPython, and familiar with deep learning frameworks such as PyTorch andTensorFlow.

-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 tohundreds-of-billions parameter scale.

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

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