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

Jabil Circuit (Singapore) Pte Ltd; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

Jabil Circuit (Singapore) Pte Ltd is seeking a core algorithm R&D engineer to advance the AI capabilities of our in-house vision platform, targeting industrial-grade performance and robustness.

You will drive research-to-production of state-of-the-art computer vision, deep learning, and multimodal techniques, including Transformers, few-shot learning, and LLM/VLM post-training, with production readiness in mind.

Qualifications

• Bachelor’s/Master’s/PhD in Computer Science, Electrical Engineering, Applied Mathematics, or related fields.
• Strong fundamentals in deep learning for computer vision, including detection and segmentation.
• Hands-on Python + C++ and PyTorch; production-ready modules.

Responsibilities

• Research, develop, and optimize computer vision algorithms across CNNs, detections, segmentation, and keypoint detection.
• Design evaluation protocols, run ablation studies, and iterate improvements (accuracy, robustness, latency).
• Build and refine Transformer-based architectures and training pipelines; explore few-shot learning and foundation-model techniques.
• Collaborate with platform teams to integrate algorithms into product modules with test coverage and stability.

Skills

Python

C++

CUDA

PyTorch

Transformers

Computer Vision

GNNs

Education

Bachelor’s / Master’s / PhD in Computer Science or related field

Tools

PyTorch

CUDA

GNN frameworks

Job description

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