Machine Learning Team Lead

Stand Insurance•San Francisco, CA

1d•$250,000 - $295,000•Onsite

About The Position

As the MLE Team Lead on the Applied Science team, you will lead the Machine Learning Engineering sub-team as it develops and deploys Stand's flagship AI capabilities spanning physics-informed machine learning, digital twins, computer vision, and spatial intelligence. You will own the technical direction, planning, and execution of critical AI initiatives, ensuring they align with business priorities, ship on schedule, and deliver measurable outcomes. This is a player-coach role, combining direct technical work and the leadership work around it: people management, project planning, cross-team coordination, and process. Reporting directly to the Chief Science Officer, you will own key projects yourself while ensuring the broader MLE team is operating effectively, growing, and delivering real impact. You are the person who looks around corners, sees what the business needs, and turns "the business needs X" into "the team builds Y." You will partner across Applied Science and the business to transform research and emerging technologies into scalable systems that directly influence underwriting, pricing, mitigation, inspection, and customer decision-making. Key initiatives include: Advancing physics-informed, AI-driven solvers and surrogate architectures, Advancing multimodal models, data augmentation, sensor fusion, and digital twin capabilities, Driving R&D programs through to validation, deployment, and business adoption, Building production-ready AI systems that accelerate, automate, and scale risk analytics.

Requirements

Proficiency with modern ML tooling and infrastructure
Experience leading engineers and technical initiatives, delivering complex projects through others as well as through direct individual contribution
Strong project ownership and execution: planning, prioritization, stakeholder coordination, and delivery of complex technical programs from concept through production
Experience combining physics-based modeling and machine learning, including simulation, scientific computing, surrogate modeling, and/or physics-informed AI approaches
Ability to operate across disciplines, connecting technical development to business objectives and customer impact, and articulating those links to the team
Strong, succinct communication and the judgment to balance research depth, delivery timelines, and business impact
Highly self-motivated, proactive, and adaptable; comfortable in fast-paced, ambiguous environments where problems, interfaces, and priorities evolve

Nice To Haves

Prior experience as a people manager, specifically in high-growth environments
Experience with computer vision, multimodal learning, or spatially-aware architectures
Familiarity with building agentic systems and LLM-powered workflows
Experience in startups or zero-to-one technology development
Knowledge of geospatial, remote sensing, or Earth observation datasets and systems

Responsibilities

Lead the Machine Learning Engineering sub-team, defining priorities, coordinating execution, and unblocking the team to deliver on critical AI initiatives
Manage and grow the team, running 1-on-1s and growth conversations, giving direct and timely feedback, managing performance, and mentoring engineers as the team scales
Design, build, and deploy machine learning systems spanning physics-informed AI, digital twins, computer vision, and spatial intelligence, contributing directly to core components
Own projects end-to-end, from problem definition and prototyping through production deployment, adoption, and ongoing performance
Extend state-of-the-art models and surrogate architectures to accelerate simulation and risk analytics workflows
Guide, support, and build scalable ML infrastructure, including data pipelines, training systems, evaluation frameworks, and production monitoring
Improve how the team works, creating process improvements and maintaining traceability
Drive cross-functional alignment, coordinating across Applied Science and the business and clearly communicating modeling decisions, tradeoffs, and status
Set a multi-year vision for the MLE team's impact and articulate how its work moves the business