Head of Engineering, AI Platform

About AiDASH
AiDASH is leading the PreventionFirst™movement for electric utilities and transforming grid resilience through its pioneering platform that unifies vegetation, asset, storm, and wildfire intelligence. Powered by SatelliteFirst™ Inspection & Monitoring, AiDASH delivers comprehensive visibility across the entire grid at the right frequency and budget, using the right data modality. More than 200 customers trust AiDASH to keep the lights on, spend where it counts, and defend every decision, Securing Tomorrow across every mile of the grid. Learn more at www.aidash.com.
The PreventionFirst movement is growing, and so is the recognition behind it. In 2026, Forbes named AiDASH one of America's Best Startup Employers for the 4th consecutive year, and TIME included AiDASH among America's Top GreenTech Companies for the 3rd year in a row. Deloitte Technology Fast 500™ ranked AiDASH No. 12 in the San Francisco Bay Area, and No. 59 overall in their selection of the top 500 for 2024.
Join us in Securing Tomorrow Together!
The Role
AiDASH is scaling fast — our satellite and AI-powered utility products are set for aggressive customer growth over the next two years, and engineering is at the heart of making it happen. We’re hiring a Head of Engineering, AI Platform to join our engineering leadership team in a newly created role. In this role, you'll build our new Data Inference AI Pipeline team from the ground up to support our rapidly growing customer base.
This is a rare opportunity to build both the team and the system from scratch: you'll shape the architecture, culture, and hiring bar of a new AI team from day one, and own the pipeline that powers our AI products as our customer base grows. In this role, you will report to the VP of Engineering and have real ownership over technical decisions, team design, and engineering practices.
Our pending acquisition by Schneider Electric will accelerate our mission as we bring our combined offering to our joint customer base, and the systems you build will power outcomes at an even greater scale.
Location: Palo Alto, CA (hybrid/2 days per week in the office)
How you'll make an impact:
Build the Data Inference AI Pipeline team
Define the team's charter, headcount plan, and hiring bar, and make the first several hires, bringing in talent from both within AiDASH and outside
Architect a multimodal data inference pipeline that scales with customer growth while keeping compute and infrastructure cost per inference efficient
Build the model training and human-in-the-loop (HITL) feedback loops that keep our AI accurate and continuously improving
Establish strong engineering practices from day one, including code review, testing, on-call, and release process
Lead and grow the team
Hire, coach, and develop engineering managers and leads, and manage performance as needed, building a high-performing, high-ownership culture
Partner with Product, Data Science, and Design to shape roadmap trade-offs and sequencing
Own technical decisions within your team's scope, and partner with the VP of Engineering on cross-org architecture decisions
Give the leadership a clear, reliable view of progress, risks, and resourcing needs
What success looks like in your first 6 months:
The Data Inference AI Pipeline team is staffed with its first core hires, from within AiDASH and outside, and running on an architecture that scales with growing data volume without a proportional rise in infrastructure cost
The team has clear ownership, a steady delivery cadence, and visible engineering practices (code review, on-call, release process) in place
Leadership has a clear, reliable read on the team's progress, risks, and resourcing needs
What we're looking for:
Minimum Qualifications
10+ years of overall software engineering experience, including 3-5+ years leading engineering teams (managing managers or senior ICs)
A track record of building a team or system from scratch, not just scaling an existing one
Hands-on experience architecting data/ML pipelines at scale, with a strong grasp of throughput, latency, and cost-efficiency trade-offs for inference workloads
Experience building modern AI systems, including reasoning frameworks, agentic capabilities, retri