ABOUT STAND

Stand is a new technology and insurance company revolutionizing how society assesses, mitigates, and adapts to climate risks. Our leadership team has extensive experience in insurance, technology, and climate science: building billions in market value at prior ventures. At Stand, we are rethinking how insurance enables proactive, science-driven resilience.
Existing insurance models often rely on broad exclusions, leaving homeowners without options. At Stand, we leverage advanced deterministic models and cutting-edge analytics to provide personalized risk assessments—helping homeowners secure coverage and take proactive steps toward resilience.

CORE SKILLS (MUST-HAVES):

• Strong foundation in ML with experience in computer vision, geospatial data, segmentation, and taking models from research to production
• Proficiency in ML frameworks (PyTorch, TensorFlow), with ability to train from scratch, fine-tune advanced architectures, and evaluate model performance across diverse datasets and production scenarios
• Familiarity with modern methods: transformers, diffusion, multimodal models, GNNs, and agentic AI frameworks (e.g., LangChain)
• Comfortable in a modern software environment: standardized dev environments (monorepos/containers), CI/CD pipelines, automated testing, code reviews, and shared libraries/SDKs
• Ability to stay current with emerging methods and apply them pragmatically to business problems
• Strong collaborator across disciplines with the ability to connect knowledge silos and foster innovation
• 5 years of Industry experience doing relevant work post Masters program in relevant field

THE ROLE:

On the Applied Science team, we develop machine learning models that power Stand’s risk analytics and climate resilience platform. Our goal is to combine AI, geospatial intelligence, and physics-based simulations into scalable tools that directly influence underwriting, pricing, and customer decision-making.
As a Machine Learning Engineer, you’ll own projects end-to-end: designing, training, and deploying models that deliver immediate and long-term business impact. You’ll work in a fast-moving, startup environment where priorities evolve quickly, collaborating across science, product, and engineering to translate technical breakthroughs into production-ready solutions.

THIS ROLE WILL:

• Own projects end-to-end from prototyping through production with emphasis on reliability and measurable outcomes
• Design, train, and deploy ML models across computer vision, geospatial data, multimodal learning, and AI-driven physics
• Fine-tune and apply state-of-the-art models to automate our simulation and remote-inspection pipeline
• Build scalable ML infrastructure including data pipelines, training methodologies, and evaluation frameworks for real-time risk analytics
• Collaborate cross-functionally with front-end developers, the Product team, and Applied Science stakeholders to integrate models into user-facing workflows
• Continuously improve model performance through monitoring, retraining, and active learning