Aleksandra Mirisola
Senior Staff UX Researcher And Manager, AI-powered Support For Advertisers at Google
Miami-Fort Lauderdale Area United States
Summary
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RockstarAleksandra Mirisola is a Senior Staff UX Researcher and manager based in the Miami-Fort Lauderdale area, leading AI-powered support initiatives for advertisers at Google with 12 years of product and engineering-adjacent experience. She blends user-centered research and cross-functional leadership to shape how complex AI support features are designed, validated, and scaled for advertisers. Before focusing full-time on UX, she has a strong engineering background contributing to prominent open-source Kubernetes projects—improving autoscaler health checks, performance tests, and monitoring integrations—bringing a rare combination of research empathy and production systems know-how. Aleksandra’s work emphasizes operational resilience and measurable metrics, reflecting her habit of surfacing technical health signals into product decisions. Colleagues rely on her to translate messy real-world behavior into actionable product directions and reliable engineering fixes. Her dual fluency in user research and backend engineering helps bridge teams to deliver robust, user-centered AI tooling.
12 years of coding experience
English, Polish, French, Spanish, Italian, German
Github Skills (24)
chaos-engineering10
monitoring10
kubernetes10
docker10
stackdriver10
apidoc10
scripting10
health-check10
dockers10
performance-testing10
script10
go10
prometheus10
api10
kubernetes-pods10
Programming languages (7)
Github contributions (5)
kubernetes/autoscaler
May 2017 - Mar 2020
Autoscaling components for Kubernetes
Role in this project:
Backend Developer
Backend DeveloperContributions:27 releases, 63 reviews, 416 commits in 2 years 10 months
Contributions summary:Aleksandra contributed significantly to the health check functionality of the Kubernetes Autoscaler. They implemented the health check endpoint, which included the addition of a liveness check and updated the metrics package to track the last activity time and successful runs for the autoscaler. They further enhanced the health check by incorporating conditions to fail if the autoscaler loop returned consistent errors or if there was inactivity. In addition, the user modified core components to improve scale down and management of pods.
k8skubernetesautoscaling
kubernetes/perf-tests
Nov 2021 - Oct 2022
Performance tests and benchmarks
Role in this project:Back-end & DevOps Engineer
Back-end & DevOps EngineerContributions:9 reviews, 11 commits, 13 PRs in 10 months
Contributions summary:Aleksandra contributed to the `kubernetes/perf-tests` repository, focusing on enhancing the generic Prometheus query measurement. They implemented features to support multiple queries, introduced a dry-run flag for testing, and added a chaos monkey measurement for simulating node failures. Additionally, the user refactored existing code to improve logging and utilized shared killedNodes sets, demonstrating both development and operational skills within the context of Kubernetes performance testing.
stress-testingbenchmarkingperformance-testsbenchmarkperformance
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