<div class="content-intro"><p><strong>About AiDASH</strong><br><br><span data-teams="true">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.</span> Learn more at <a href="https://www.aidash.com." target="_blank">www.aidash.com.</a></p> <p><span data-contrast="auto">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. </span><a href="https://www2.deloitte.com/us/en/pages/technology-media-and-telecommunications/topics/north-america-technology-fast-500.html?utm_source=bngaiwebsite&utm_medium=referral&utm_campaign=evolve-banner"><span data-contrast="none"><span data-ccp-charstyle="Hyperlink">Deloitte Technology Fast 500</span></span></a><span data-contrast="auto">™ ranked AiDASH No. 12 in the San Francisco Bay Area, and No. 59 overall in their selection of the top 500 for 2024.</span><span data-ccp-props="{"335551550":0,"335551620":0}"> </span></p> <p><span data-contrast="auto">Join us in Securing Tomorrow Together!</span><span data-ccp-props="{"335551550":0,"335551620":0}"> </span></p></div><h4><strong><span data-contrast="none">The Role</span></strong><span data-ccp-props="{}"> </span></h4> <p><span data-teams="true">We are looking for a dedicated ML Model Evals & Benchmarking Engineer to bridge the gap between machine learning research and high-performance production systems. In this role, you will own the end-to-end evaluation lifecycle, designing rigorous offline and online evaluation frameworks, defining robust model performance metrics, and architecting the scalable infrastructure needed to benchmark throughput, latency, resource utilization, and statistical accuracy across diverse ML/AI models.</span></p> <p><strong><span data-contrast="none"><br>How You’ll Make an Impact:</span></strong></p> <ul> <li data-leveltext="" data-font="Symbol" data-listid="11" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" data-aria-posinset="1" data-aria-level="1"><strong><span data-contrast="auto">Own model quality before production:</span></strong><span data-contrast="auto"> <span data-ccp-props="{"134233117":false,"134233118":false,"335559738":0,"335559739":0}"><span data-teams="true">Establish standard evaluation frameworks using statistical metrics (e.g., Precision, Recall, F1-Score, ROC-AUC) tailored to specific business domains. </span></span>Build validation frameworks for computer vision models (preferably satellite or geospatial). Catch regressions in statistical metrics before they reach customers.</span><span data-ccp-props="{"134233117":false,"134233118":false,"335559738":0,"335559739":0}"> </span></li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="11" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" data-aria-posinset="2" data-aria-level="1"><strong><span data-contrast="auto">Build version-aware model regression:</span></strong><span data-contrast="auto"> 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.</span><span data-ccp-props="{"134233117":false,"134233118":false,"335559738":0,"335559739":0}"> </span></li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="11" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" data-aria-posinset="3" data-aria-level="1"><strong><span data-contrast="auto">Make data quality a first-class gate:</span></strong><span data-contrast="auto"> 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.</span><span data-ccp-props="{"134233117":false,"134233118":false,"335559738":0,"335559739":0}"> </span></li> </ul> <ul> <li data-leveltext="" data-font="Symbol" data-listid="11" data-list-defn-props="{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" data-aria-posinset="4" data-aria-level="1"><strong><span data-contrast="auto">Validate inference at scale:</span></strong><span data-contrast="auto"> <span data-teams="true">Design, build, and scale distributed evaluation pipelines and testing harnesses capa