Machine Learning Engineer

Position: Staff Machine Learning Engineer
About AiDASH

AiDASH is an enterprise AI company and the leading provider of vegetation risk intelligence for electric utilities. Powered by proprietary Vegetation

AI™ technology, AiDASH delivers a unified remote grid inspection and monitoring platform that uses a Satellite First 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 human

AIty™. Learn more at   

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 Green Tech Companies 2024”.
Deloitte Technology Fast 500  ™ recently ranked us  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’re looking for a seasoned  Staff Machine Learning Engineer  to shape and scale the backbone of our production ML ecosystem. In this role, you will architect high-performing ML systems that power our geospatial intelligence platform, transforming large-scale satellite and aerial imagery into actionable insights. You’ll lead end-to-end ownership—from model deployment and MLOps to infrastructure design—while partnering closely with data science, platform engineering, and product teams to deliver reliable, scalable, and cost-efficient ML solutions.

If you thrive at the intersection of deep technical expertise, system design, and cross-functional collaboration, this role is for you. This role is based out of  Gurgaon or Banaglore.

How you'll make an impact:

ML System Architecture & Production Deployment
Design, build, and maintain end-to-end ML pipelines for batch processing of satellite and aerial imagery
Deploy and scale ML models in production on AWS infrastructure, leveraging services like Sage Maker, Bedrock,or custom-built solutions
Implement MLflow for experiment tracking, model versioning, and model registry management
Architect batch inference systems optimized for throughput and cost-efficiency
Work with geospatial data formats and coordinate reference systems
Collaborate with data scientists to transition models from research to production
Partner with platform engineering to build scalable compute, GPU clusters, and storage layers

ML Operations & Reliability
Implement comprehensive model monitoring systems to track performance degradation and data drift
Design and execute canary deployments and A/B testing frameworks for safe model rollouts
Build active learning pipelines to continuously improve model performance with minimal labeling effort
Establish model evaluation frameworks and performance benchmarking processes

Create alerting and observability systems for production ML workloads

Technical Leadership
Mentor ML engineers and data scientists on best practices for production ML
Drive technical decision-making on ML infrastructure and tooling
Collaborate with platform and data engineering teams to optimize the ML stack
Establish engineering standards and contribute to architectural roadmaps

What we’re looking for:

5+ years of experience in machine learning engineering with 2+ years in a senior or lead capacity
Proven track record deploying and maintaining ML systems in production using AWS services (Sage Maker,Lambda, ECS/EKS, S3, etc.)
Strong hands-on experience with tools like MLflow, WandB, or similar for experiment tracking and model management
Deep expertise in image segmentation and computer vision techniques using frameworks like PyTorch or Tensor Flow
Production experience with ensemble models (xgboost, lightgbm, RF)

ML Operations Expertise
Experience implementing model monitoring, drift detection, and alerting systems
Hands-on experience…