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.
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