Xin Yi
AI Engineer at Amazon
- Handle
- @xinnyi
- Role
- Software Engineer
- Company
- Amazon (verified: @amazon.com)
- Location
- Bay Area
- Languages
- English, Chinese
- Open to
- AI Platform / ML training & Inference related work
About
AI Engineer at Amazon focused on LLM agent, AI/ML systems, and distributed systems.
Experience across embedding retrieval, model serving, high-throughput backend infrastructure, LLM post-training, and GPU performance optimization.
Interested in AI Infrastructure, ML Systems, and LLM training and inference.
Experience
- Software Engineer at Amazon
Jun 2025 to now - Seattle
• Led EU + APAC expansion (19 marketplaces) of Audience Targeting Agent, an automated keyword/audience/category recommendation system for advertiser’s campaigns; architected split-region EU deployment to resolve conflict between data residency, Bedrock AgentCore and Ads Service availability with secure cross-region invocation. • Built marketplace-aware cross-lingual keyword retrieval by deploying a multilingual embedding model and re-ingesting 8.5M localized keyword records through new OpenSearch neural pipelines across three regions. • Delivered end-to-end observability for the multi-stage agent serving path using OpenTelemetry and CloudWatch; reduced token usage by 40% via compressed few-shot prompts, and cut TTFT by 27% using Gemma3-4B for ranking. • Co-designed and implemented the migration of audience-permission lookup and segment synchronization from a legacy graph-based model to a DynamoDB two-table design, increasing supported throughput from 2 to 10,000 TPS (5,000×). - Software Engineer Intern at Amazon
May 2024 to Nov 2024 - Seattle
• Built a distributed user-activity dual-stage ingestion pipeline: GraphQL→SNS→SQS→Lambda→transaction DB with early filtering and elastic concurrency, plus a DynamoDB Streams-triggered aggregator Lambda with hybrid batching; sustained ∼150ms average latency and zero observed errors at 2× peak production load. • Designed retry and failure isolation mechanisms; automated multi-region deployment via AWS CDK (TypeScript) CI/CD with CloudWatch monitoring. - Machine Learning Framework Engineer at Huawei
Jul 2022 to Oct 2022 - Remote
Dilation CPU Operators Development for MindSpore Open-Source Framework • Designed Python front-end APIs and C++ back-end logic for Dilation and two backpropagation operators, supporting 12 tensor precisions with relative error under 0.002% vs. NumPy. • Optimized performance by flattening 3D tensors to reduce branch divergence and adapting static-shape inputs to dynamic shape for flexible memory management. - Machine Learning Engineer Intern at ML4SCI
Jun 2022 to Aug 2022 - Remote
• Focused on the problem of data sparsity in image-based particle recognition methods; represented particle jets images as graphs and explored various channel combinations as node features; determined the most appropriate representation strategies. • Defined edges in a static or dynamic manner and computed connectivity in static graphs using KNN or radius neighbors. • Built various GNN model architectures for end-to-end tau particle recognition using current cutting-edge methods in graph deep learning research, including graph convolution, graph SAGE, graph attention, and dynamic edge convolution. • Analyzed model performance from multiple perspectives and provided possible explanations. Benchmarked model inference on GPUs and provided guidance for users through documentation and code. • Developed CLI tools in Python to automate training and inference, improving development efficiency by 40%. - Research Assistant at Huazhong University of Science and Technology
Jan 2023 to Apr 2023 - Wuhan, China
GPU Runtime Scheduling for GNN Training • Built a persistent CUDA task-pool scheduler for GNNAdvisor with warp-level workload balancing, occupancy-aware execution, and kernel fusion to reduce launch and global-memory overhead. • Benchmarked GCN/GIN GPUkernels across 15 graph datasets, validating against PyG and achieving up to 5.3% speedup over hardware scheduling.
Education
- Texas A&M University
Master of Computer Science
2023 to 2025
GPA 3.8/4.0 - Huazhong University of Science & Technology
Bachelor of Engineering, Computer Science
2019 to 2023
cGPA 3.7/4.0, Major 3.95/4.0
Skills
- Java
- Python
- C++/C
- TypeScript
- Kafka
- Spark
- PyTorch
- TensorFlow
- CUDA
- Docker
- Kubernetes
- AWS
- Machine Learning
- LLM
- Agent
- Post Training
- Machine Learning System
Links
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