SDE 3 - Machine Learning Engineer
AiDash
About AiDASH
AiDASH is an enterprise AI company and the leading provider of vegetation risk intelligence for electric utilities. Powered by proprietary VegetationAI™ technology, AiDASH delivers a unified remote grid inspection and monitoring platform that uses a SatelliteFirst approach to identify and address vegetation and other threats to the grid. With a prevention-first strategy to mitigate wildfire risk and minimize storm impacts, AiDASH helps more than 140 utilities reduce costs, improve reliability, and lower liability across their networks. AiDASH exists to safeguard critical utility infrastructure and secure the future of humanAIty™. Learn more at www.aidash.com.
We are a Series C growth company backed by leading investors, including Shell Ventures, National Grid Partners, G2 Venture Partners, Duke Energy, Edison International, Lightrock, Marubeni, among others. We have been recognized by Forbes two years in a row as one of “America’s Best Startup Employers.” We are also proud to be one of the few software companies in Time Magazine’s “America’s Top GreenTech Companies 2024”. Deloitte Technology Fast 500™ recently ranked us at No. 12 among San Francisco Bay Area companies, and No. 59 overall in their selection of the top 500 for 2024.
Join us in Securing Tomorrow!
The Role
We are looking for a Senior Machine Learning Engineer to design, build, and deploy scalable ML and deep learning systems for Earth observation, asset intelligence, and operational decision-making. You will collaborate closely with data scientists, data engineers, and product teams to convert research models into reliable production-grade systems powering AiDash’s core products.
How you'll make an impact:
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Design and implement ML pipelines for large-scale geospatial, temporal, and structured datasets
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Productionize models for classification, segmentation, object detection, and predictive analytics across vegetation, infrastructure, and environmental domains
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Build scalable training, validation, and deployment workflows on cloud infrastructure (AWS, Kubernetes, Snowflake, etc.)
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Collaborate with domain experts and data engineers to enhance model performance and data quality
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Lead experiments in model optimization, generalization across geographies, and multimodal fusion (satellite + IoT + text data)
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Mentor junior engineers and contribute to best practices for reproducible ML development
Who we're looking for:
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6+ years of experience in applied ML, including deep learning model development and deployment
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Strong skills in Python, PyTorch/TensorFlow, and ML pipeline tools (Airflow, MLflow, Kubeflow)
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Experience with geospatial data formats (raster, vector), computer vision, or time-series analysis
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Working knowledge of cloud environments (AWS/Azure/GCP) and containerized deployments
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Solid understanding of feature engineering, data preprocessing, and model monitoring
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Strong communication and collaboration skills; ability to translate research into production
We are proud to be an equal-opportunity employer. We are committed to embracing diversity and inclusion in our hiring practices, and we promote a work environment where everyone, from any race, color, religion, sex, sexual orientation, gender identity, or national origin, can do their best work.
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