Snapchat/GiGL
Apr 2025 – Feb 2026
11 Months active
Languages Used
C++PythonJinjaSQLShellYAMLBashGit
Technical Skills
Backend DevelopmentData LoadingData ProcessingDistributed SystemsGraph Neural NetworksMachine Learning
Kyle Montemayor
PROFILE
Kyle Montemayor
Kevin Montemayor developed distributed graph processing infrastructure for the Snapchat/GiGL repository, focusing on scalable data loading, training, and inference workflows for large heterogeneous graphs. He engineered robust distributed sampling frameworks and integrated Vertex AI orchestration, enabling seamless end-to-end experiments across cloud and on-prem environments. Using Python, PyTorch, and Google Cloud Platform, Kevin modernized the storage and compute stack, implemented memory-efficient data handling, and improved CI reliability through automated testing and error handling. His work emphasized maintainability and operational safety, delivering flexible configuration management, enhanced documentation, and developer tooling that accelerated experimentation and reduced runtime failures in production graph workloads.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
143Total
Bugs
16
Commits
143
Features
47
Lines of code
41,769
Activity Months11
Your Network
24 peopleSame Organization
@snapchat.com16
Work History
February 2026
13 Commits • 2 Features
Feb 1, 2026February 2026: Delivered major modernization of Snapchat/GiGL's distributed graph storage and compute stack (GiGL), enabling scalable, reliable graph processing across distributed environments. Completed migration of GLT DistServer to GiGL DistServer, improved distributed sampling, storage integration, and end-to-end inference across storage and compute resources. Added end-to-end heterogeneous graph store inference example and updated related loaders to support GiGL storage paths. Strengthened testing framework and CI reliability through standardized GiGL test migrations, robust failure handling, and utilities refactor. Implemented targeted enhancements such as get_ablp_input support and proper port allocation for Graph Store mode. Overall, these efforts deliver faster, more reliable graph analytics with lower operational risk and easier maintainability.
13 Commits • 2 Features
Feb 1, 2026February 2026: Delivered major modernization of Snapchat/GiGL's distributed graph storage and compute stack (GiGL), enabling scalable, reliable graph processing across distributed environments. Completed migration of GLT DistServer to GiGL DistServer, improved distributed sampling, storage integration, and end-to-end inference across storage and compute resources. Added end-to-end heterogeneous graph store inference example and updated related loaders to support GiGL storage paths. Strengthened testing framework and CI reliability through standardized GiGL test migrations, robust failure handling, and utilities refactor. Implemented targeted enhancements such as get_ablp_input support and proper port allocation for Graph Store mode. Overall, these efforts deliver faster, more reliable graph analytics with lower operational risk and easier maintainability.
February 2026
January 2026
12 Commits • 5 Features
Jan 1, 20262026-01 GiGL monthly performance summary focusing on distributed graph processing enhancements, heterogeneous data support, and improved inference workflows. The month delivered a robust distributed sampling framework, expanded graph-store capabilities for heterogeneous datasets, and strengthened training input infrastructure, enabling scalable, reliable experimentation and faster issue resolution in production.
January 2026
12 Commits • 5 Features
Jan 1, 20262026-01 GiGL monthly performance summary focusing on distributed graph processing enhancements, heterogeneous data support, and improved inference workflows. The month delivered a robust distributed sampling framework, expanded graph-store capabilities for heterogeneous datasets, and strengthened training input infrastructure, enabling scalable, reliable experimentation and faster issue resolution in production.
December 2025
9 Commits • 2 Features
Dec 1, 2025December 2025 was anchored in enabling scalable distributed graph processing within Snapchat/GiGL, delivering end-to-end distributed graph store capabilities and strengthening reliability for production workloads. Key deliverables enable graph workloads to run from trainer/inferencer workflows, improve memory efficiency on compute nodes, and harden deployment safeguards. The month also advanced testing resilience and operational safety to reduce runtime issues and speed up development cycles.
9 Commits • 2 Features
Dec 1, 2025December 2025 was anchored in enabling scalable distributed graph processing within Snapchat/GiGL, delivering end-to-end distributed graph store capabilities and strengthening reliability for production workloads. Key deliverables enable graph workloads to run from trainer/inferencer workflows, improve memory efficiency on compute nodes, and harden deployment safeguards. The month also advanced testing resilience and operational safety to reduce runtime issues and speed up development cycles.
December 2025
November 2025
15 Commits • 5 Features
Nov 1, 2025November 2025 Snapchat/GiGL monthly summary: Delivered end-to-end Vertex AI orchestration and Graph Store integration, optimized data processing with a new BigQuery table copy method, and completed a migration to the gigl.nn module. Strengthened CI/test observability and refreshed licensing/typing for better maintainability. Focused on delivering business value through scalable distributed graph processing and cost-aware data operations.
November 2025
15 Commits • 5 Features
Nov 1, 2025November 2025 Snapchat/GiGL monthly summary: Delivered end-to-end Vertex AI orchestration and Graph Store integration, optimized data processing with a new BigQuery table copy method, and completed a migration to the gigl.nn module. Strengthened CI/test observability and refreshed licensing/typing for better maintainability. Focused on delivering business value through scalable distributed graph processing and cost-aware data operations.
October 2025
5 Commits • 2 Features
Oct 1, 2025Month 2025-10: Delivered distributed training and cluster configuration enhancements for Snapchat/GiGL, expanded graph-loading flexibility with multi-edge-type supervision, and hardened the test infrastructure to improve reliability and CI value.
5 Commits • 2 Features
Oct 1, 2025Month 2025-10: Delivered distributed training and cluster configuration enhancements for Snapchat/GiGL, expanded graph-loading flexibility with multi-edge-type supervision, and hardened the test infrastructure to improve reliability and CI value.
October 2025
September 2025
12 Commits • 3 Features
Sep 1, 2025September 2025 (Month: 2025-09) focused GiGL development on scalable Vertex AI workflows, robust distributed data loading for large graphs, and improved CI/test stability. Deliveries targeted business value in reliability, throughput, and cloud integration, enabling safer GPU-accelerated training and inference at scale while accelerating PR validation.
September 2025
12 Commits • 3 Features
Sep 1, 2025September 2025 (Month: 2025-09) focused GiGL development on scalable Vertex AI workflows, robust distributed data loading for large graphs, and improved CI/test stability. Deliveries targeted business value in reliability, throughput, and cloud integration, enabling safer GPU-accelerated training and inference at scale while accelerating PR validation.
August 2025
9 Commits • 5 Features
Aug 1, 2025August 2025: Delivered developer-focused enhancements and governance improvements for Snapchat/GiGL, enabling clearer onboarding, safer data management, and improved automation. Notable work includes: enhanced trainer documentation and README; enforced GCS for exported configurations; Uri class enhancement with a pathlib-like '/' operator; added /help PR automation command for better discoverability; Vertex AI deployment enhancements with region override and pipeline labeling. These efforts improved maintainability, cost attribution, deployment traceability, and automation usability.
9 Commits • 5 Features
Aug 1, 2025August 2025: Delivered developer-focused enhancements and governance improvements for Snapchat/GiGL, enabling clearer onboarding, safer data management, and improved automation. Notable work includes: enhanced trainer documentation and README; enforced GCS for exported configurations; Uri class enhancement with a pathlib-like '/' operator; added /help PR automation command for better discoverability; Vertex AI deployment enhancements with region override and pipeline labeling. These efforts improved maintainability, cost attribution, deployment traceability, and automation usability.
August 2025
July 2025
36 Commits • 13 Features
Jul 1, 2025July 2025 monthly summary: Focused on boosting runtime robustness, enabling easier experimentation, and enhancing educational/demo tooling. Delivered automated port inference for DistABLPLoader and the hetero inference loop to reduce manual configuration; expanded notebook ecosystem with CORA/DBLP notebooks, visuals, tests, and an instructional training loop; introduced orchestration capabilities in toy notebooks to demonstrate end-to-end trainer/inferencer workflows; implemented configuration-driven data splits and code quality improvements to simplify experimentation and improve maintainability; and strengthened reliability and deployment hygiene via test separation, notebook test fixes, and data-copy utilities to support data workflows.
July 2025
36 Commits • 13 Features
Jul 1, 2025July 2025 monthly summary: Focused on boosting runtime robustness, enabling easier experimentation, and enhancing educational/demo tooling. Delivered automated port inference for DistABLPLoader and the hetero inference loop to reduce manual configuration; expanded notebook ecosystem with CORA/DBLP notebooks, visuals, tests, and an instructional training loop; introduced orchestration capabilities in toy notebooks to demonstrate end-to-end trainer/inferencer workflows; implemented configuration-driven data splits and code quality improvements to simplify experimentation and improve maintainability; and strengthened reliability and deployment hygiene via test separation, notebook test fixes, and data-copy utilities to support data workflows.
June 2025
18 Commits • 3 Features
Jun 1, 2025June 2025 performance summary for Snapchat/GiGL: Delivered foundational graph data loading and labeling enhancements, stabilized CI/CD pipelines, and advanced repository hygiene and documentation. The work improves reliability, reproducibility, and developer productivity for graph-based experiments and deployments.
18 Commits • 3 Features
Jun 1, 2025June 2025 performance summary for Snapchat/GiGL: Delivered foundational graph data loading and labeling enhancements, stabilized CI/CD pipelines, and advanced repository hygiene and documentation. The work improves reliability, reproducibility, and developer productivity for graph-based experiments and deployments.
June 2025
May 2025
12 Commits • 5 Features
May 1, 2025May 2025 highlights for Snapchat/GiGL: delivered substantial anchor-based link prediction (ABLP) enhancements, including the DistABLPLoader and conversion transforms, with fixes to shutdown handling and loading robustness. improved graph label-to-edge generation and heterogeneous graph support through new helpers and upgraded type annotations. introduced the CoraFromGCS dataset loader to fetch data from Google Cloud Storage, boosting load performance and source flexibility. strengthened CI and development tooling (mypy, stubs, test timeouts, deflaking efforts) and established a local Scala/Coursier setup for Linux to improve developer onboarding and reliability. rolled back a complex distributed dataset port change to simplify worker connections and reduce fragility. Overall impact: faster data loading, more robust graph processing, improved tooling, and greater stability in distributed workflows.
May 2025
12 Commits • 5 Features
May 1, 2025May 2025 highlights for Snapchat/GiGL: delivered substantial anchor-based link prediction (ABLP) enhancements, including the DistABLPLoader and conversion transforms, with fixes to shutdown handling and loading robustness. improved graph label-to-edge generation and heterogeneous graph support through new helpers and upgraded type annotations. introduced the CoraFromGCS dataset loader to fetch data from Google Cloud Storage, boosting load performance and source flexibility. strengthened CI and development tooling (mypy, stubs, test timeouts, deflaking efforts) and established a local Scala/Coursier setup for Linux to improve developer onboarding and reliability. rolled back a complex distributed dataset port change to simplify worker connections and reduce fragility. Overall impact: faster data loading, more robust graph processing, improved tooling, and greater stability in distributed workflows.
April 2025
2 Commits • 2 Features
Apr 1, 2025Month: 2025-04 | Snapchat/GiGL Key features delivered: - DistNeighborLoader batched input support: refactored input handling for batched node data, added shuffling and last-batch-drop parameters, and introduced tests. (Commit fe3d3788e9c20f1a1edaffc5e3ea41cbdfe7f6ce) - Anchor node label retrieval with padding for jagged tensors: added get_labels_for_anchor_nodes to retrieve and pad labels for anchor nodes across homogeneous and heterogeneous graphs, including positive and optional negative labels, with padding for jagged tensors to ensure consistent tensor shapes. (Commit 35cd8e2553784348375e1aa62adb016a73e41e13) Major bugs fixed: - None reported this month. Overall impact and accomplishments: - Enhanced data loading efficiency and scalability for graph training workflows by enabling batched input processing in DistNeighborLoader, while ensuring consistent tensor shapes across graph variants. Added test coverage to prevent regressions, contributing to more reliable experimentation and faster iteration cycles. Technologies/skills demonstrated: - PyTorch Geometric data loading and batched processing - Handling of jagged tensors and padding strategies for consistent shapes - Test-driven development and refactoring for batch-oriented pipelines - Experience with graphs in heterogeneous/homogeneous contexts Business value: - Faster, more scalable model training workflows with larger batched graphs; reduced preprocessing complexity; improved maintainability of the data-loading stack.
2 Commits • 2 Features
Apr 1, 2025Month: 2025-04 | Snapchat/GiGL Key features delivered: - DistNeighborLoader batched input support: refactored input handling for batched node data, added shuffling and last-batch-drop parameters, and introduced tests. (Commit fe3d3788e9c20f1a1edaffc5e3ea41cbdfe7f6ce) - Anchor node label retrieval with padding for jagged tensors: added get_labels_for_anchor_nodes to retrieve and pad labels for anchor nodes across homogeneous and heterogeneous graphs, including positive and optional negative labels, with padding for jagged tensors to ensure consistent tensor shapes. (Commit 35cd8e2553784348375e1aa62adb016a73e41e13) Major bugs fixed: - None reported this month. Overall impact and accomplishments: - Enhanced data loading efficiency and scalability for graph training workflows by enabling batched input processing in DistNeighborLoader, while ensuring consistent tensor shapes across graph variants. Added test coverage to prevent regressions, contributing to more reliable experimentation and faster iteration cycles. Technologies/skills demonstrated: - PyTorch Geometric data loading and batched processing - Handling of jagged tensors and padding strategies for consistent shapes - Test-driven development and refactoring for batch-oriented pipelines - Experience with graphs in heterogeneous/homogeneous contexts Business value: - Faster, more scalable model training workflows with larger batched graphs; reduced preprocessing complexity; improved maintainability of the data-loading stack.
April 2025
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Quality Metrics
Correctness89.6%
Maintainability86.6%
Architecture86.4%
Performance81.6%
AI Usage24.4%
Skills & Technologies
Programming Languages
BashC++GitJSONJavaJinjaJupyter NotebookMakefileMarkdownPython
Technical Skills
AI IntegrationAI model trainingAPI integrationApache BeamBackend DevelopmentBigQueryBuild AutomationBuild System ConfigurationBuild ToolsCI/CDCloud ComputingCloud Computing (GCP)Cloud EngineeringCloud StorageCloud Storage (GCS)
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