Senior / Staff Computer Vision & AI Algorithm Engineer (Core R&D)

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