SDET 3 - Model Evaluation

About AiDASH
AiDASH is leading the PreventionFirst™movement for electric utilities and transforming grid resilience through its pioneering platform that unifies vegetation, asset, storm, and wildfire intelligence. Powered by SatelliteFirst™ Inspection & Monitoring, AiDASH delivers comprehensive visibility across the entire grid at the right frequency and budget, using the right data modality. More than 200 customers trust AiDASH to keep the lights on, spend where it counts, and defend every decision, Securing Tomorrow across every mile of the grid. Learn more at www.aidash.com.
The PreventionFirst movement is growing, and so is the recognition behind it. In 2026, Forbes named AiDASH one of America's Best Startup Employers for the 4th consecutive year, and TIME included AiDASH among America's Top GreenTech Companies for the 3rd year in a row. Deloitte Technology Fast 500™ ranked AiDASH No. 12 in the San Francisco Bay Area, and No. 59 overall in their selection of the top 500 for 2024.
Join us in Securing Tomorrow Together!
The Role
The Platform ML team deploys and validates every ML model behind Aidash’s products. These models process satellite imagery, geospatial data, and weather data. As an SDET-3, you will own end-to-end quality for these models, from training data preparation through inference pipelines to performance at production scale. You will validate both functional correctness and scaling behavior, and you will use agentic AI tools to move faster without lowering engineering standards.
How You’ll Make an Impact:
Own model quality before production. Build validation frameworks for computer vision models (preferably satellite or geospatial). Catch regressions in accuracy, precision/recall, and IoU before they reach customers.
Build version-aware model regression. Set up pipelines that compare inference outputs across model versions on curated golden datasets. Every release should come with a clear, data-backed go/no-go signal.
Make data quality a first-class gate. Validate training and evaluation datasets for label quality, class balance, geographic and seasonal coverage, leakage, and drift. Ensure models are tested on data that reflects real-world conditions.
Validate inference at scale. Design load and benchmark tests for inference services and batch pipelines. Find the throughput, latency, and cost limits on AWS and Kubernetes before customers do.
Harden the platform end-to-end. Automate API, contract, and data-pipeline tests across distributed services. Use logs, metrics, and traces to speed up root-cause analysis.
Raise the bar for the team. Strengthen the shared automation frameworks and set standards for maintainable, non-flaky tests. Show how tools like Cursor and Claude can speed up test authoring, triage, and reporting while staying under human review.
Influence release decisions. This is a high-visibility role. Your quality signals will directly shape what ships across Aidash’s product lines
What we’re looking for:
We are looking for a senior QA engineer with deep backend automation experience who can validate ML models and backend systems end-to-end, and who actively uses agentic AI development tools (e.g., Cursor, Claude) to accelerate test design, automation, debugging, and reporting — without compromising fundamentals, coverage, or engineering quality.
8–10 years in software testing and test automation, with strong ownership of backend system quality