Data Science Leader

2 days ago

Palo Alto, California, United States AiDash Full time
About AiDash

AiDash is a pioneering climate tech company that empowers critical infrastructure industries to become climate-resilient and sustainable. Leveraging satellite data and artificial intelligence, our full-stack SaaS solutions enable customers in electric, gas, and water utilities, transportation, and construction to transform asset inspection and maintenance processes.

Our mission is to help clients achieve their sustainability goals while reducing costs and improving reliability. With a strong presence in the market, we have been recognized by Forbes as one of 'America's Best Startup Employers' for two consecutive years and are proud to be featured in Time Magazine's 'America's Top GreenTech Companies 2024'.

The Role

We are seeking an experienced Principal Machine Learning Engineer to join our team. In this role, you will play a key part in developing and enhancing our ML infrastructure and platforms. You will work with cutting-edge technologies, expanding your knowledge and skills rapidly. Your collaboration with diverse teams across the company will ensure the direct impact of your contributions on our products and customers.

  • Design and build scalable platforms for training and inference of ML models.
  • Develop and integrate data pre-processing and post-processing workflows for seamless model deployment.
  • Build robust model monitoring services on top of the inference platform to ensure optimal performance.
  • Create platforms for large-scale model evaluation and grading.
  • Develop advanced tools for model experimentation to accelerate innovation.
  • Design and implement sampling strategies to effectively assess model performance.
  • Oversee the entire ML lifecycle, including design, experimentation, development, deployment, monitoring, and maintenance.
  • Develop reusable workflows for Data Science models and integrate them with production systems, ensuring efficiency and minimal redundancy.
  • Deploy production-ready code and actively participate in code reviews to maintain high-quality standards.
  • Refactor services to improve code quality, runtime efficiency, and resource optimization.
  • Build automation and active learning frameworks to streamline model retraining processes.

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