Standinsurance

<a target="_blank" rel="noopener noreferrer ugc" href="https://www.standinsurance.com/careers/why-stand/">Why Join Stand</a>: At Stand, you’ll help build a new class of global property protection. We use advanced physics and AI to model catastrophic risk at the asset level, then automate underwriting and mitigation before loss occurs. Insurance is simply the current delivery mechanism. The real product is a scalable risk engine, our <a target="_blank" rel="noopener noreferrer ugc" href="https://frontier.standinsurance.com">Stand World Model</a>.We stay when traditional insurers exit. We model what others approximate. And we build systems that change outcomes, not just prices. Our <a target="_blank" rel="noopener noreferrer ugc" href="https://www.standinsurance.com/vision">leadership team</a> includes former successful founders and CEOs from Metromile, PolicyGenius, WePay, and HotelTonight, bringing deep experience in building and scaling high-growth companies.Background: The property insurance industry is built to price loss after it happens. It relies on coarse proxies, backward-looking data, and manual processes, then accepts damage as unavoidable.Stand takes a different approach. We simulate how real-world catastrophes affect individual properties, translate that into actionable decisions, and automate the business around it. The result is a platform that can underwrite what others can’t and operate with far less friction.Role SummaryWe are building the self-driving insurer: a national carrier whose standard operating procedure is explicit, instrumented, and handed to agents one proven step at a time — so the book grows without the org.Every one of those decisions runs on data, and every one of them is only as good as the number underneath it. That's the precondition this role owns. Not a dashboard. The warehouse, the definitions, and the query surfaces that underwriting, pricing, and leadership all resolve to — and that our agents will read from long before they're trusted to write.Concretely, the platform is four things:A warehouse where every dataset moves at its own speed. Today we have a centralized analytics database in Postgres, rebuilt hourly in full. It works, and it's why you start on the second problem instead of the first. You migrate it: dbt for models, a real orchestrator for scheduling, per-dataset cadence and freshness SLAs, snapshots on the entities whose history matters. Bind state on minutes, vendor pulls and reference data daily.A trust framework and diagnostics console. Reconciliation between each system of record and the warehouse — row counts, premium totals, status parity — running as tests on every load and failing the run, not as a monthly spot check. A console where anyone, not just you, can see whether a dataset is fresh, whether it reconciled, and which dashboards are affected when it didn't. Turning a discrepancy someone noticed into a permanent test should take an afternoon.A shared semantic layer. One registry of typed, versioned, permission-aware entities and metrics. A metric means one thing, defined once, in version control. Deciding what a metric should mean isn't your call; making it easy for an actuary or underwriter to codify it once, so the next person doesn't redefine it in a dashboard, is your job. The agentic query surface is built on this layer and can't see past it: an agent answers in natural language, shows the query it ran, and reaches nothing its invoking user couldn't reach directly.Data that's safe to be creative with. An automated production → sanitized pipeline producing a de-identified but faithful copy of the book — consistent synthetic identities, preserved distributions, intact joins, edge cases kept rather than smoothed away. It becomes the default seed for local, dev, and staging, and the default substrate for anyone prototyping against real-shaped data. Raw PII access becomes the exception with an audit trail.You'll partner closely with Actuarial and Underwriting, who ask the questions, and with Applied Science, whose models both consume this data and produce more of it. Working to understand what insights are missing and build a pathway to get them the information they need. You start by understanding their problem, then you solve it, and then you build a harness that can help them solve problems themselvesThis is a foundations role. Your success is measured by the questions other people answer without you: how fast an actuary can rate a cohort, how confidently an underwriter can trust a stage count, and how quickly an engineer can prototype against realistic data without filing a request.What you'll ship<ul><li>First 30 days: Warehouse chosen and justified in writing — cost at our volume, latency, how it reads from operational Postgres, who administers it. A dbt project that reproduces today's tables, with an automated parity check against the existing store that passes before anything changes. Orchestrator running the current schedule unchanged, now with a dependency graph and per-model run history.</li><li>First 3 months: A governance-first pipeline that empowers stakeholders to self-serve metric definitions and targets, independently promoting ideas to productionized, tested models. Launch of the diagnostics console, enabling non-engineers to monitor data health, freshness SLAs, and reconciliation status without engineering intervention.</li><li>First 6 months: A semantic layer that dashboards, analysts, and agents all resolve to, with permissions enforced centrally and covered by tests. A sanitized production mirror refreshing on schedule and seeding every non-production environment. Leadership pipeline, loss ratio, and premium reporting all coming from one source, produced without engineering in the loop.</li></ul>Core competencies (Required)<ul><li>Min of 12 years of experience in industry</li><li>Proven experience migrating a live analytics store onto a warehouse in production — including the cutover and the parity proof, not a greenfield build.</li><li>dbt in production: incremental models, snapshots, tests, and the operational habits around them.</li><li>Hands-on experience with a real orchestrator (Dagster, Airflow, Prefect) — dependency graphs, backfills, retries, and being paged when a load fails.</li><li>Track record of building data-quality or observability infrastructure: reconciliation suites, freshness monitoring, lineage, alerting people didn't turn off.</li><li>Proficiency designing schemas and metric definitions that non-engineers can actually use.</li><li> Familiarity with authorization and access-control patterns for data.</li><li>Comfort and experience in product discovery — working directly with the people who need the answer, not from a ticket.</li></ul>Nice-to-have skills<ul><li>Insurance data experience. Policy administration systems (Socotra, Guidewire, Duck Creek), written vs. earned premium, loss and premium triangles, reserving and development, statutory or bureau reporting, or having partnered closely with actuaries.</li><li>Geospatial data at scale — parcels, hazard layers, imagery, point clouds — with PostGIS, H3, or similar. Our unit of analysis is a specific building on a specific parcel.</li><li>Curating data for ML and evals — training sets, labeled decision logs, feature stores, and the discipline of keeping a holdout honest.</li><li>Reverse ETL — pushing modeled data back into the operational surfaces where people actually work.</li><li>Early-stage experience where you were the first data hire and had to choose what not to build.</li></ul>You're not a fit if<ul><li>You have less than 12 years of experience in industry</li><li>Your data experience is primarily building dashboards on top of models someone else owned.</li><li>You want a fully specified backlog. This role is defined by ambiguity for the first year.</li><li>You'd rather design the ideal warehouse than migrate the working one, or you'd leave the old pipeline running indefinitely because removing it feels risky.</li></ul>The annual base salary range for full-time employees in this position is $240,000 to $295,000 + meaningful Equity Grant. Compensation decisions are dependent on several factors including, but not limited to, an individual’s qualifications, location where the role is to be performed, internal equity, and alignment with market data.Benefits:<ul><li>Above-market Health, Dental, and Vision coverage</li><li>Weekly lunch stipend</li><li>Flexible time off + holidays</li><li>401(k) plan</li><li>Commuter benefits</li><li>PAT &amp; MAT Leave</li><li>Short-Term and Long-Term Disability</li><li>Monthly team gatherings</li><li>In-office perks</li></ul>AI in the Interview ProcessThoughtful use of AI tools is expected and valued at Stand. Candidates should be prepared to discuss how they use AI, how they evaluate its output, and how it informs their work. Strong communication, sound judgment, and the ability to clearly articulate experience and decision-making remain core requirements. Some parts of the interview process are designed to assess independent thinking, communication, and problem-solving. If you plan to use an AI assistant or LLM during any portion of an interview, please discuss it with your interviewer in advance. Work AuthorizationCandidates must be authorized to work in the U.S. Stand does not sponsor new work visas. We can consider candidates on TN visas, O-1A visas, or H-1B transfers with three years or more remaining.Equal Opportunity EmploymentStand is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. We believe that diversity enriches the workplace, and we are committed to growing our team with the most talented and passionate people from every community.We are committed to providing reasonable accommodations for qualified individuals. If you require assistancePursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

Member of the Technical Staff - Data Engineer

<a target="_blank" rel="noopener noreferrer ugc" href="https://www.standinsurance.com/careers/why-stand/">Why Join Stand</a>: At Stand, you’ll help build a new class of global property protection. We use advanced physics and AI to model catastrophic risk at the asset level, then automate underwriting and mitigation before loss occurs. Insurance is simply the current delivery mechanism. The real product is a scalable risk engine, our <a target="_blank" rel="noopener noreferrer ugc" href="https://frontier.standinsurance.com">Stand World Model</a>.We stay when traditional insurers exit. We model what others approximate. And we build systems that change outcomes, not just prices. Our <a target="_blank" rel="noopener noreferrer ugc" href="https://www.standinsurance.com/vision">leadership team</a> includes former successful founders and CEOs from Metromile, PolicyGenius, WePay, and HotelTonight, bringing deep experience in building and scaling high-growth companies.Background: The property insurance industry is built to price loss after it happens. It relies on coarse proxies, backward-looking data, and manual processes, then accepts damage as unavoidable.Stand takes a different approach. We simulate how real-world catastrophes affect individual properties, translate that into actionable decisions, and automate the business around it. The result is a platform that can underwrite what others can’t and operate with far less friction.Role Summary:As a Machine Learning Engineer on the Applied Science team, you will design, train, and deploy Stand's flagship AI capabilities, with a central focus on the multimodal meshing of our Stand World Model with powerful language models. This work brings physical simulation, rich 3D representations of real assets, and broader business context together into models that can reason across all of them at once, in support of better underwriting, pricing, and mitigation decisions.This is a hands-on, high-ownership position on the Machine Learning team within Stand Applied Science. You will own modeling work end-to-end, from architecture and training strategy through evaluation and production deployment, and partner closely with the Platform team to ensure the agentic harness and workflows your models plug into deliver strong results in production.Your partnerships will extend across the business, mirroring the breadth of the model's inputs: collecting technical insight from subject matter experts and other MLEs, and institutional judgment from underwriting, pricing, mitigation, inspection, and customer decision-making.Key initiatives include:<ul><li>Designing and training multimodal model architectures that jointly reason over physical, spatial, and business-context data</li><li>Building frameworks that let these models act as agents within nuanced workflows, making complex tool calls that include interacting with our world-modeling stack</li><li>Developing retrieval and similarity capabilities over learned representations of real-world assets and their multi-layered complexities</li><li>Standing up the training-data pipelines, evaluation harnesses, and production monitoring that take these models from prototype to production, collaborating closely with Applied Science infrastructure engineers to build out proper tooling</li></ul>What You'll Do:<ul><li>Design, build, and deploy machine learning systems spanning multimodal learning, physics-informed AI, digital twins, and spatial intelligence, contributing directly to core business impact</li><li>Own projects end-to-end, from problem definition and prototyping through production deployment, adoption, and ongoing performance monitoring</li><li>Develop rigorous evaluation frameworks that weigh model judgments against real business outcomes</li><li>Build on and extend scalable ML infrastructure</li><li>Partner with Stand’s Platform team on the model-harness interface</li><li>Drive cross-functional alignment, communicating decisions, tradeoffs, and status</li></ul>Core Skills (Must-Haves):<ul><li>Deep hands-on experience designing and training multimodal models, fusing heterogeneous data (e.g., 3D/vision, simulation outputs, tabular, and text) into shared representations; strong VLM exposure</li><li>Experience building and post-training multi-step LLM agents that call external tools and interact with environments, including evaluating if a trajectory reflects appropriate judgment and user constraints</li><li>A record of bringing models of this class to production: training at scale, evaluation, deployment, and iteration on live systems</li><li>Experience applying ML to complex physical systems. We are agnostic to the domain: atmospheric, molecular, protein, robotics, fluid dynamics, or other physics-grounded modeling all carries over</li><li>Strong project ownership and execution: planning, prioritization, and delivery of complex technical work</li><li>Ability to operate across disciplines, connecting technical development to business objectives</li><li>Strong, succinct communication and judgment to balance R&amp;D, delivery timelines, and business impact</li><li>Highly self-motivated, proactive, and adaptable; comfortable in fast-paced, ambiguous environments</li></ul>Nice to Haves:<ul><li>Experience with embodied AI— training agents to navigate in 3D environments</li><li>Experience with retrieval and embedding systems: vector search and similarity in latent space</li><li>Experience with geometric deep learning: point clouds, meshes, and spatially-aware architectures</li><li>Familiarity with physics-informed AI and surrogate modeling across a number of domains</li><li>Experience in startups or zero-to-one technology development</li><li>Knowledge of geospatial, remote sensing, or Earth observation datasets</li></ul>Compensation:The annual base salary range for full-time employees in this position is $250,000 to $295,000 + meaningful Equity Grant. Compensation decisions are dependent on several factors including, but not limited to, an individual’s qualifications, location where the role is to be performed, internal equity, and alignment with market data.Benefits:<ul><li>Above-market Health, Dental, and Vision coverage</li><li>Weekly lunch stipend</li><li>Flexible time off + holidays</li><li>401(k) plan</li><li>Commuter benefits</li><li>PAT &amp; MAT Leave</li><li>Short-Term and Long-Term Disability</li><li>Monthly team gatherings</li><li>In-office perks</li></ul>AI in the Interview ProcessThoughtful use of AI tools is expected and valued at Stand. Candidates should be prepared to discuss how they use AI, how they evaluate its output, and how it informs their work. Strong communication, sound judgment, and the ability to clearly articulate experience and decision-making remain core requirements. Some parts of the interview process are designed to assess independent thinking, communication, and problem-solving. If you plan to use an AI assistant or LLM during any portion of an interview, please discuss it with your interviewer in advance. Work AuthorizationCandidates must be authorized to work in the U.S. Stand does not sponsor new work visas. We can consider candidates on TN visas, O-1A visas, or H-1B transfers with three years or more remaining.Equal Opportunity EmploymentStand is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. We believe that diversity enriches the workplace, and we are committed to growing our team with the most talented and passionate people from every community.We are committed to providing reasonable accommodations for qualified individuals. If you require assistancePursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

Standinsurance

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Member of the Technical Staff - Data Engineer

Machine Learning Engineer - Multimodal Modeling

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