Staff Machine Learning Engineer

About AiDASHAiDASH 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”. 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 RoleWe’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 DeploymentDesign, build, and maintain end-to-end ML pipelines for batch processing of satellite and aerial imageryDeploy and scale ML models in production on AWS infrastructure, leveraging services like SageMaker, Bedrock,or custom-built solutionsImplement MLflow for experiment tracking, model versioning, and model registry managementArchitect batch inference systems optimized for throughput and cost-efficiencyWork with geospatial data formats and coordinate reference systemsCollaborate with data scientists to transition models from research to productionPartner with platform engineering to build scalable compute, GPU clusters, and storage layersML Operations & ReliabilityImplement comprehensive model monitoring systems to track performance degradation and data driftDesign and execute canary deployments and A/B testing frameworks for safe model rolloutsBuild active learning pipelines to continuously improve model performance with minimal labeling effortEstablish model evaluation frameworks and performance benchmarking processesCreate alerting and observability systems for production ML workloadsTechnical LeadershipMentor ML engineers and data scientists on best practices for production MLDrive technical decision-making on ML infrastructure and toolingCollaborate with platform and data engineering teams to optimize the ML stackEstablish engineering standards and contribute to architectural roadmapsWhat we’re looking for:5+ years of experience in machine learning engineering with 2+ years in a senior or lead capacityProven 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 managementDeep expertise in image segmentation and computer vision techniques using frameworks like PyTorch or TensorFlowProduction experience with ensemble models (xgboost, lightgbm, RF)ML Operations ExpertiseExperience implementing model monitoring, drift detection, and alerting systemsHands-on experience with canary deployments, A/B testing and Shadow deployments for ML modelsKnowledge of active learning strategies and human-in-the-loop ML systemsStrong understanding of model evaluation metrics, bias detection, and performance analysisTechnical SkillsExpert-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 PachydermStrong software engineering fundamentals: unit/integration testing, CI/CD, version control, observability, designpatternsExperience with containerization (Docker, Kubernetes) for model deploymentGood to have experience with ML Orchestration tools like Kubeflow, Vertex AI, etcNice to have experience with GPUs: scheduling GPU jobs, optimizing GPU performance, memory profilingWe 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.Read our Privacy Policy here:

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