Sr. ML Engineer – ML & Applied AI

關於Gap集團

我們旗下品牌在世界上的重要鴻溝之間架設橋梁。Old Navy讓時尚觸手可及，確保每個人都能獲得價廉物美的時尚單品。Athleta致力於釋放每一位女性的潛能，不論身材、年齡或種族。Banana Republic相信永續的奢華體驗屬於每個人。Gap更是啓發了全世界，讓所有人透過認真製作的現代服飾必備品展現其獨特個性。    

這一簡單的想法——即每個人都需要以自己的方式獲得歸屬感——是我們作為一間公司以及制定決策的核心。​我們的團隊由成千上萬名來自世界各地具有冒險精神、全局視野並為顧客、社區和地球行善的員工組成。如果妳具備全局視野，快速學習、無畏創新、大膽領導等品質，歡迎加入我們。

About the Role

Gap Inc. is seeking a Senior Machine Learning Engineer with 10+ years of experience to design, build, and scale production-grade machine learning and AI systems that power data-driven decision making across the enterprise.

This role is focused on end-to-end ML system ownership, including data pipelines, feature engineering, model training, deployment, monitoring, and continuous optimization. You will lead the development of scalable ML platforms, drive best practices in MLOps, and enable reliable, high-performance model inference in both batch and real-time environments.

The ideal candidate combines strong software engineering expertise with deep ML knowledge and has experience building robust, scalable ML systems in production, including modern applications involving large language models (LLMs) and agent-based AI systems.

What You'll Do

• Architect and build scalable, production-grade ML systems from experimentation to deployment and lifecycle management
• Design and implement end-to-end ML pipelines, including data ingestion, feature engineering, training, validation, and inference
• Develop and maintain high-performance model serving systems using APIs (e.g., FastAPI) for real-time and batch inference
• Lead the design and implementation of feature stores and reusable feature pipelines across teams
• Build and optimize distributed data processing workflows using Spark, Databricks, or similar platforms
• Implement and enforce MLOps best practices, including CI/CD pipelines, automated retraining, model versioning, and experiment tracking
• Design and manage model monitoring and observability frameworks to track performance, drift, latency, and system health
• Drive strategies for model retraining, drift detection, and continuous improvement
• Collaborate closely with data engineers, platform teams, and product stakeholders to integrate ML solutions into production systems
• Contribute to the adoption of modern AI capabilities, including LLMs, vector databases, retrieval-augmented generation (RAG), and agentic workflows
• Ensure high standards of code quality, testing, documentation, and reproducibility

Who You Are

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
• 10+ years of experience in machine learning, software engineering, or related roles, with significant experience in production ML systems
• Strong programming expertise in Python and solid software engineering fundamentals (data structures, system design, APIs)
• Extensive experience with ML frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow
• Proven experience designing and deploying scalable ML pipelines and services in production
• Hands-on experience with model serving frameworks and API development (e.g., FastAPI, Flask)
• Strong experience with containerization (Docker) and orchestration platforms such as Kubernetes
• Experience working with cloud platforms (GCP, AWS, or Azure) and building cloud-native ML solutions
• Deep understanding of ML lifecycle management, including training, evaluation, deployment, monitoring, and retraining
• Experience implementing CI/CD pipelines for ML workflows and managing version control systems (Git)
• Strong experience with SQL and distributed data processing frameworks (e.g., Spark, PySpark)
• Excellent problem-solving skills and ability to design scalable, maintainable systems