百威雷科技有限公司
AI Software Engineer / Machine Learning Deployment Engineer
9/15 Updated
Array
English Required
Full-time
Entry-level
English RequiredSalary & Location
Monthly SalaryNT$ 60,000+
台北市中山區
Required
Language Requirements
English
聽/中等、說/中等、讀/精通、寫/精通
Work Experience
不拘
Job Description
[Job Title]: Machine Learning Deployment Engineer
[About Us]:
Founded by ex-googlers and started at Stanford’s StartX program, PowerArena now has offices in the States, Taiwan, Hong Kong and China. We are a fast-growing, research-driven company building AI solutions that helps manufacturing corporations and factories assembly operations overcome the challenges they face in productions every day.
We are a fast-growing, research-driven company building AI solutions that helps corporate and factories overcome the challenges they face every day. Using novel machine learning techniques, we are revolutionizing the industry and have a track record of building things that others have ruled out as impossible. Our team is our best asset. We work with smart and talented individuals, who all enjoy a high degree of responsibility and independence in structuring their work. The team are looking for passionate data scientist who is keen to work with like-minded individuals in a rapidly evolving environment.
=====[Job Description]=====
[Position Overview]:
The ML Deployment Engineer will be responsible for deploying machine learning algorithms developed by our data scientists into production environments. This includes both backend servers and edge devices. The ideal candidate will have a strong background in machine learning, software engineering, and experience with deployment and monitoring of ML models in diverse environments.
[Key Responsibilities]:
- Deploy ML Algorithms:
1. Collaborate with data scientists to understand the algorithms and deployment requirements.
2. Implement and optimize ML models and related logics in backend servers and edge devices.
- Backend Deployment:
1. Set up and maintain ML models on server infrastructures.
2. Ensure seamless integration with existing backend systems.
3. Monitor and optimize model performance in production environments.
- Edge Device Deployment:
1. Adapt and deploy ML models to edge devices ensuring efficient performance.
2. Manage the deployment pipeline for edge computing environments.
3. Troubleshoot and resolve issues related to model performance on edge devices.
- Automation and CI/CD:
1. Develop and maintain CI/CD pipelines for ML model deployment.
2. Automate deployment processes to ensure fast and reliable model updates.
- Monitoring and Maintenance:
1. Monitor deployed models to ensure they meet performance and accuracy benchmarks.
2. Conduct regular maintenance and updates of deployed models.
3. Implement logging and alerting mechanisms to proactively address potential issues.
- Documentation and Collaboration:
1. Document deployment processes and configurations.
2. Work closely with cross-functional teams including data scientists, software engineers, and DevOps teams.
3. Provide support and guidance to team members on best practices for model deployment.
- Share technical knowledge with team members.
[Qualifications]:
- Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
- Proven experience in deploying machine learning models to production environments.
- Strong programming skills in languages such as Python, Java, or C++.
- Experience with ML frameworks such as TensorFlow, PyTorch, or similar.
- Experience with model and code optimization.
- Proficiency in containerization technologies (Docker, Kubernetes).
- Experience with edge computing and deploying models on edge devices.
- Familiarity with CI/CD pipelines and DevOps practices.
- Strong problem-solving skills and ability to troubleshoot deployment issues.
- Excellent communication and teamwork skills.
[What We Offer]:
- Flexible working environment
- Competitive salary and benefits package.
- Opportunity to work on cutting-edge projects with real-world impact.
- Collaborative and innovative work environment.
- Professional development and training opportunities.
Number of Openings
1~1人
Educational Requirements
大學(學院)以上
Field of Study Requirements
資訊工程相關、其他數學及電算機科學相關、電機電子工程相關
Work Schedule
09:00~18:00
Leave Policy
週休二日
Job Skills
Software Engineering Development
Firmware Engineering Development
Software Programming
Structured Programming
Modular System Design
Machine Learning
Machine Learning
Deep Learning
Artificial Intelligence
AI
Algorithm Design
System Architecture Planning and Maintenance
軟體工程系統開發 韌體工程開發 軟體程式設計 結構化程式設計 模組化系統設計 Machine Learning 機器學習 深度學習 人工智慧 AI 演算法設計 系統架構規劃與維護
Job Category
Back-End Engineer
Software Engineer
AI Engineer
台北市中山區
其它軟體及網路相關業
We give manufacturers the Power to change everything.
PowerArena is founded by ex-Googlers. We started at Stanford’s StartX program with teams around the world from Taiwan, United States, China, Mexico to Hong Kong. With three out of the global top five EMS enterprises enhancing production efficiency with HOP, our AI Vision solutions are now live in hundreds of factories across major manufacturing countries.
PowerArena’s Human Operation Platform (HOP) digitizes your manual production line with AI vision. HOP provides real-time transparent data for labor-intensive production lines. With 24/7 data collection, AI vision analysis, and instant traceability, HOP redefines your production line. Identifying bottlenecks and provides a valuable data base for SOP compliance, line balance, and IPQC. HOP supports every decision you make and help you achieve data ownership, paving a smoother route for your smart manufacturing journey.
PowerArena 提供 AI 視覺驅動得智慧製造解決方案。
PowerArena 由前 Google 創始員工創立，從史丹佛創業孵化器（Stanford’s StartX）開始了我們的旅程，現於台灣、美國、墨西哥、中國、香港等地都成立服務據點。我們的智慧製造解方已踏足六個國家，且導入三家全球前五大 EMS 廠，PowerArena AI 視覺系統幫助多位企業夥伴提升生產效能。
PowerArena 人因作業平台 (Human Operation Platform, HOP) 用 AI 可視化生產作業，為勞力密集的產線，提供即時、透明化的生產資訊。HOP 具備 24/7 影像搜集、AI 視覺分析與隨時回溯等特點，完整掌握產線資訊，幫助辨識作業瓶頸，為 SOP 遵守、線平衡表現和製程品管改善，提供有價值數據基礎。HOP 支持每一項決策，讓管理者握有數據主權，幫助達成智慧製造，自信營運。
