Job Summary 

We are hiring a core algorithm R&D engineer to develop and advance the key AI capabilities of our internally developed vision platform. You will drive research-to-production delivery of state-of-the-art computer vision, deep learning, and multimodal foundation model techniques, focusing on industrial-grade performance, robustness, and efficiency. 

Key Responsibilities 

Core Vision Algorithm R&D (Deep Learning + Transformers) 

• Research, develop, and optimize computer vision algorithms across: 
  CNN-based classification, anomaly detection, Siamese networks, object detection, rotated object detection, semantic segmentation, instance segmentation, keypoint detection. 

• Build and improve Transformer-based detection/recognition architectures and training pipelines. 

• Design evaluation protocols, run ablation studies, and iterate based on measurable improvements (accuracy, robustness, latency). 

Few-shot / Small-sample Learning for Industrial Use Cases 

• Own R&D for few-shot rotated detection, segmentation, and anomaly detection—aiming to train effective models from only a few images. 

• Explore and implement methods such as meta-learning, prompt-/prototype-based learning, retrieval-enhanced approaches, and foundation-model feature adaptation for industrial inspection scenarios. 

LLM / VLM Fine-tuning & Reinforcement Learning (Post-training) 

• Understand LLM/VLM principles and implement practical post-training pipelines:  

• Supervised fine-tuning (SFT), parameter-efficient fine-tuning (e.g., LoRA/PEFT), alignment methods (e.g., RLHF/DPO-like approaches), evaluation harnesses and safety/quality checks.  

• Build reproducible training workflows (data curation, experiment tracking, model versioning, deployment readiness). 

Vector / Graph-based Learning for CAD/PCB & Structured Data 

• Research and develop models beyond raster images for vector data scenarios (e.g., engineering drawings, PCB schematics/layouts), aiming to outperform image-based baselines. 

• Apply graph neural networks (GNNs) and vector/geometric representations to tasks such as component understanding, connectivity reasoning, and structured recognition.  

High-performance Implementation & Productionization 

• Write efficient, maintainable code in C++ and Python for training/inference pipelines and algorithm modules. 

• Develop high-performance compute kernels and optimizations using SIMD and/or CUDA, profiling and improving runtime, memory use, and throughput. 

• Collaborate with platform/software teams to integrate algorithms into product modules and ensure test coverage, stability, and maintainability. 

Paper Reading & Reproducibility 

• Regularly read and analyze top-tier papers; identify key contributions and reproduce core algorithms in code. 

• Deliver internal technical notes and share learnings with the team. 

Required Qualifications 

• Bachelor’s / Master’s / PhD in Computer Science, Electrical Engineering, Applied Mathematics, or related fields (industry experience may substitute). 

• Strong fundamentals and hands-on experience in deep learning for computer vision, including detection and segmentation.  

• Solid engineering ability with Python + C++; capable of building clean training code （with Pytorch) and production-ready modules.  

• Practical experience with performance optimization and acceleration (one or more of CUDA / SIMD / parallel computing). 

• Ability to communicate effectively in both Chinese (Mandarin) and English as the successful person will have to liaise with our counterparts in China.