At Jabil (NYSE: JBL), we are proud to be a trusted partner for the world's top brands, offering comprehensive engineering, supply chain, and manufacturing solutions. With 60 years of experience across industries and a vast network of over 100 sites worldwide, Jabil combines global reach with local expertise to deliver both scalable and customized solutions. Our commitment extends beyond business success as we strive to build sustainable processes that minimize environmental impact and foster vibrant and diverse communities around the globe.

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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Jabil, including its subsidiaries, is an equal opportunity employer and considers qualified applicants for employment without regard to race, color, religion, national origin, sex, sexual orientation, gender identity, age, disability, genetic information, veteran status, or any other characteristic protected by law.

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