Building the foundational storage and compute infrastructure that powers high-precision mapping and machine learning model training for autonomous vehicle technology at Cruise.

Spearheaded the development of a Kubernetes-native compute platform to run large-scale ML training and computer vision workloads, powering AR map generation and supporting a variety of GenAI and data processing use cases across the organization.

Drove core infrastructure enhancements, including:

• Migrating the platform to Ray

• Integrating Volcano for gang scheduling and optimized resource allocation

• Implementing zero-downtime node pool deployments

• Designing job queuing systems to ensure fair and effective resource usage

Solved critical scaling challenges for clusters exceeding 7,000 nodes, enabling large-scale ML training, massive data processing pipelines, and real-time AR operations with high reliability.

Led the design and execution of a high-throughput data pipeline processing 100M+ images daily, building a global 3D map to power advanced AR experiences.

Tech Stack: Kubernetes, Ray, Argo Workflows, Volcano, Cloud Infrastructure (AWS/GCP).

As a member of the Shared AR team, I played a crucial role in developing the backend infrastructure for delivering cross-platform, persistent, and multiplayer AR experiences on the Niantic Lightship platform.

My expertise in distributed systems was leveraged to design and implement key components of the system, including the service discovery module, a peer-to-peer and server-side message routing layer, map storage and indexing, and session-based data sharding using Redis. These efforts have led to the successful deployment of the Multiplayer AR Buddy feature in Pokemon Go, demonstrating the robustness and reliability of the infrastructure I helped build.

Teaching assistant for Operating System Design and Implementation (15-410/605)