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 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

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”. ™ 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’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 SageMaker, 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 processesCreate 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 (SageMaker,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 TensorFlow Production experience with ensemble models (xgboost, lightgbm, RF)

ML Operations Expertise Experience implementing model monitoring, drift detection, and alerting systems Hands-on experience with canary deployments, A/B testing and Shadow deployments for ML models Knowledge of active learning strategies and human-in-the-loop ML systems Strong understanding of model evaluation metrics, bias detection, and performance analysis

Technical Skills Expert-level Python programming with ML libraries (scikit-learn, PyTorch/TensorFlow, NumPy, pandas, etc) Experience with distributed batch processing frameworks (Airflow, Step Functions, Argo Workflows, Spark,Dask, Ray or similar) Proficiency with AWS ML ecosystem and infrastructure-as-code (Terraform, CloudFormation) Hands-on experience with dataset versioning tools such as DVC, LakeFS, Delta Lake, Quilt, or Pachyderm Strong software engineering fundamentals: unit/integration testing, CI/CD, version control, observability, designpatterns Experience with containerization (Docker, Kubernetes) for model deployment Good to have experience with ML Orchestration tools like Kubeflow, Vertex AI, etc Nice to have experience with GPUs: scheduling GPU jobs, optimizing GPU performance, memory profiling

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.

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