Staff Product Manager - Applied AI Workflow

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 www.aidash.com. 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 Role Reporting to the VP of Product Management & Process Excellence, you'll own the production workflows that transform raw satellite imagery into customer-grade insights — designing, automating, and continuously improving the pipelines that sit at the heart of how AiDASH delivers value. You'll start by going deep on our Vegetation Management Workflow (IVMS) — where complexity is highest and the automation upside is largest. From there, the scope grows to cover Asset Inspection & Monitoring (AIMS) and Climate Risk Intelligence (CRIS). You won't be designing customer-facing product features. You won't be building the internal platform (that's our Platform PM, your closest peer). You'll be designing the operating model that connects them — the steps, frameworks, and policies that govern how an insight gets produced, who or what handles each step, and where humans stay in the loop. How you'll make an impact: Workflow design across products: Define what the production workflow looks like end-to-end for each product: the sequence of steps, the cohort logic (which customers / geographies / products take which path), the handoffs, and the SLAs Step-level frameworks: Author the operating frameworks for individual steps — e.g., the image acquisition framework (when do we re-order? from which vendor? what freshness threshold?), the model QC framework (what's the sampling strategy by model age, terrain, sensor?), and similar for every critical step Autonomy and human-in-the-loop policy: Decide where the workflow runs autonomously and where humans intervene. Set and own the confidence thresholds at which model output is trusted enough to drop QC. The technical specifics — sampling strategies, model evaluation methods, HITL mechanics — are owned by a pod of applied AI data scientists and analysts you'll partner closely with. You own the policy decision; they own the underlying technical work that informs it Cohort logic and CS alignment: Decide which customers get which workflow flavor. CS and leadership are key stakeholders you'll bring along Requirements to Platform PM: Translate workflow design into clear system requirements (e.g., "at step X, capture labels with confidence scores and reviewer ID"). Platform PM owns the system spec; you own that the workflow as designed produces the data and outcomes you need KPIs : Own the operating metrics — cost per insight, cycle time, % auto-resolved, manual touches per job, quality against SLA. Track and move them What Success looks like in 12 months, A significant IVMS workflow transformation is shipped, stable in production, and delivering its promised cost and automation impact A clear, sequenced plan exists for moving IVMS toward an autonomous-by-default workflow, with human intervention narrowed to a well-defined slice Manual-touch volume on IVMS is materially down vs. baseline; the cost-per-insight curve bends Every critical workflow step has a metric, a target, and a dashboard. The org can answer "how is the workflow performing this week?" without a Slack thread DS, Platform, CS, and GIS Ops consistently align to your decisions without escalation How You Operate: Shape the question before deciding the answer: When handed an ambiguous problem, you don't just solve it as posed — you reframe it, sharpen the metric, and tell us when we're optimizing for the wrong thing First-principles process thinker: You can look at a 20-step workflow, ask why step 7 exists, and not lose nuance in the process Metric-native: You reach for a measurement before a meeting. You don't ship a workflow without a way to tell whether it's working Comfortable being the decider : When cohort logic or automation policy is contested, you make the call and defend it. You don't outsource hard calls upward by default Credible with technical peers: DS, Platform PM, and engineering find you a strong partner. You can hold a substantive conversation about model performance, confidence thresholds, and system constraints Write well, think in writing: Your primary artifacts are documents — workflow charters, frameworks, decision logs Influence without authority: CS, GIS Ops, the applied AI pod, and Platform don't report to you. You move them through clarity, credibility, and shared metrics Owner mentality: When the workflow underperforms, you don't say "the model was off" or "execution slipped." You own the outcome and find where the design failed What we're looking for: 9-13 years of total experience, with at least 4-6 years as a Product Manager At least one tour owning an internal, operational, or platform-style product — not exclusively customer-facing feature PM A track record of cross-functional ownership at scale — not just shipping, but changing how the org made decisions in a domain Demonstrated ownership of operating KPIs (cost, cycle time, throughput, automation %, quality) under real accountability Nice to have: Experience working on workflows or products that combine model output with human review, and comfort making decisions that depend on model performance Background in geospatial / remote sensing / satellite imagery, utilities, climate-tech, or labeling pipelines Experience scaling a function from zero at a Series B–D company in a resource-constrained environment What this role is not: Not a feature PM role: You won't be designing what utilities see in the AiDASH product. That's owned by our IVMS / AIMS / CRIS PMs Not a platform or internal-tools PM role: You won't be writing PRDs for the labeling tool, QC dashboard, or routing engine. That's owned by our Platform PM — your peer, not your scope. Not an operations role: You won't run the workflow at scale. CS is the operating muscle that runs the workflow you design; GIS Ops is a separate execution function Not a process consultant or Op Excellence role: You don't map AS-IS / TO-BE flows and hand them over. You own the workflow as a product — its design, metrics, and evolution — and you're accountable to outcomes Not a data science role: You don't build models or design technical model evaluation strategies. You partner with the applied AI pod that does, and you make the policy calls their work informs Not customer-facing: You won't be in front of customers directly. The customer voice reaches you through CS and feature PMs Not a pure strategy or architect role: You'll be hands-on with frameworks, specs, and decisions — and driving adoption of the workflow 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. Read our Privacy Policy here: https://www.aidash.com/policy/privacy-policy/