Work

Flagship contributionProject Glasswing [NDA]

Project Glasswing [NDA] · OpenShift multicluster lifecycle management, AI orchestration, validation, and failure recovery.

// field note

A deletion request is not a deletion. It is a distributed protocol whose intermediate states need to be observable.

• Built Go, Python, and Bash tooling for OpenShift multicluster lifecycle management, reducing manual cluster-cleanup steps by 28%, improving teardown observability, and detecting orphaned Kubernetes resources across 94 namespaces.
• Developed an OpenShift AI orchestration project in Go and Python to manage cluster resources, detect degraded health, surface remediation paths, and target lower idle memory allocation; standardized AI-assisted workflows through CLAUDE.md repository conventions.
• Eliminated more than 45% of flaky multicluster validation tests by isolating race conditions in cluster-state polling, reconciliation timing, and asynchronous cleanup, then added 8 regression test files covering 19 provisioning, deletion, cleanup, and recovery cases.
• Diagnosed and fixed a recurring cluster-deletion edge case by tracing controller logs, resource-state transitions, finalizers, owner references, garbage collection, and deletion sequencing, reducing average root-cause investigation time by 15%.
• Proposed and shipped a pipeline deployment upgrade that substantially reduced container YAML expansion and cut average testing time by 30%.

Performance work in the vectorized execution paths of Meta’s open-source query engine.

// field note

Profiles narrow opinions into evidence. The useful optimization was the allocation inside the hot loop, not the most interesting-looking code.

• Profiled an aggregation hot path with perf and flame graphs, then reduced CPU overhead by avoiding repeated DecodedVector construction and unnecessary valueAt() calls, improving a microbenchmark by 8%.
• Updated an operator implementation to skip rows excluded by SelectivityVector and follow ExecCtx conventions, preventing subtle failures when filters reduce the active row set.

Internal tooling for the case-management platform used across support operations.

// field note

Reliability begins at the contract boundary: invalid input, partial failure, retry behavior, and the state callers are allowed to observe.

• Built and shipped an internal developer and support tool that automated access provisioning for lower-level test environments using Java and SQL Server on custom C++ infrastructure automation.
• Implemented and maintained REST APIs for call-center workflows used by more than 1,000 internal users, improving reliability through input validation, error handling, and consistent API contracts.
• Built a fault-tolerant service using Raft consensus, providing linearizable reads and writes with automatic failover, metrics, and tracing.

Compiler optimization research for hardware-efficient datacenter workloads.

// field note

Hardware constraints belong inside the optimizer. Treating cache, NUMA, and bandwidth as afterthoughts leaves performance to chance.

• Built and optimized compiler passes for SIMD vectorization, loop tiling, and cache-aware scheduling, improving end-to-end latency by 32.478% on representative benchmarks.
• Extended an LLVM/MLIR compilation pipeline with fusion, common subexpression elimination, and strength-reduction transforms, reducing memory traffic and increasing throughput by 15.821%.
• Implemented hardware-aware cost models for cache and NUMA behavior, instruction throughput, and memory bandwidth to guide auto-tuning and scheduling for CPU and GPU kernels.