About AiDash
AiDash is making critical infrastructure industries climate-resilient and sustainable with satellites and AI. Using our full-stack SaaS solutions, customers in electric, gas, and water utilities, transportation, and construction are transforming asset inspection and maintenance – and complying with biodiversity net gain mandates and carbon capture goals. Our customers deliver ROI in their first year of deployment with reduced costs, improved reliability, and achieved sustainability goals. Learn more at www.aidash.com.
We are a Series C climate techstartup backed by leading investors (including National Grid Partners, G2 Venture Partners, Lightrock, BGV, Marubeni, among others), and by our customers-turned-advocates (Duke Energy & National Grid Partners, 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 climate software companies in Time Magazine’s “America’s Top GreenTech Companies2024”.
Join us in creating a greener, cleaner, and safer planet from space!
The Role
We are looking for a Staff Machine Learning Engineer to develop and enhance our ML infrastructure and platforms, streamlining the process of building, deploying, and monitoring machine learning models. In this role, you will work with cutting-edge technologies, rapidly expanding your knowledge and skills. You will collaborate with diverse teams across the company, and will see the direct impact of your contributions on our products and customers.
How you'll make an impact?
- Design and develop scalable training and inference platforms.
- Develop and Integrate data pre and post processing workflows for ML models.
- Develop model monitoring service on top of inference platform.
- Develop platforms to grade models at scale.
- Develop model experimentation tools.
- Develop sampling strategy for assessing model performance.
- Oversee the entire lifecycle (design, experimentation, development, deployment, monitoring, and maintenance).
- Create reusable workflows for the Data Science Models and integrate them with the production features while avoiding redundancies.
- Deploy code to production and engage in code reviews.
- Refactor service to improve code quality, runtime efficiency and resource optimisation.
- Develop automation / active Learning frameworks for retraining models.
- Lead and mentor a team of software engineers: Foster a collaborative and high-performing team environment, providing guidance and support to your engineers to help them achieve their full potential.
What we're looking for?
- Bachelor's / Master’s Degree in Computer Science, Mathematics & Computing, Electrical Engineering, or a related field with a min of 8+ years of experience.
- Experience with machine learning ecosystem.
- Rock solid experience in monitoring models and data in a production setup.
- Experience in sampling strategies for different types of models and use cases.
- Experience in grading models at scale.
- Experience in designing and developing distributed training and inference platforms using distributed computing (PySpark, Kubeflow, Kubernetes).
- Extensive experience in writing computer programs in Python.
- Experience with Docker.
- Experience in developing tools to evaluate model performance (MLFlow,Tensorboard,Wandb).
- Experience in MLOps (AWS, GCP, Azure ).
- Experience in handling large datasets for training (HDFS,Datalakes,NoSQL/SQL).
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.
We are committed to providing an inclusive and accessible interview experience for all candidates. Please let us know if you require any accommodation during the interview process, and we will make every effort to meet your needs.
