Staff MLOps Engineer

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
Our satellite and AI-powered products are changing how utilities see and protect the grid, and we're gearing up for aggressive customer growth over the next two years. That growth runs on AI at scale. We're building a brand-new Data Inference AI Pipeline team to deliver it, and we're looking for a Staff MLOps Engineer to architect its operational backbone from the very first line of infrastructure code.
As the very first engineer on this team, you'll start with a blank canvas and a rare chance to build something that lasts. You'll shape how our models are deployed, how the pipeline scales and recovers under load, and what it costs to run. Just as importantly, you'll help define the culture, engineering standards, and ways of working that every future teammate inherits. This is a hands-on, high-leverage individual contributor role: your decisions will set the foundation the entire team builds on, and you'll be the go-to technical authority on MLOps as the team grows. You'll report to the Director of Engineering for the Data Inference AI Pipeline team.
Our pending acquisition by Schneider Electric will accelerate our mission as we bring our combined offering to our joint customer base, and the infrastructure you build will power outcomes at an even greater scale.
Location: This is a hybrid role based in Palo Alto, CA, requiring 2 days per week in the office.
How you'll make an impact:
Deployment
Own the model deployment pipeline end-to-end: packaging, versioning, rollout, and rollback for ML models moving from training to production
Build and maintain deployment infrastructure on SageMaker (endpoints, batch transform, multi-model/multi-container hosting), and evaluate where alternatives such as self-hosted serving or other managed options are a better fit
Set the CI/CD pattern for model releases, including automated testing, staged rollout, and canary/shadow deployments
Scalability & elasticity
Design inference infrastructure that scales seamlessly with real-time and batch demand
Right-size compute (CPU/GPU/Inferentia or equivalent) for each model and workload to balance latency and cost
Load-test the pipeline and build capacity plans that stay ahead of customer growth milestones
Cost
Own the cost-per-inference metric: instrument it, report on it, and drive it down as volume scales
Build cost-awareness into architecture decisions, including autoscaling policies, spot/on-demand mix, and model size/quantization trade-offs
Failover & resilience
Design for graceful degradation and failover across regions and availability zones for inference serving
Define and test disaster-recovery procedures, and run regular failure-injection and chaos exercises
Build monitoring, alerting, and on-call runbooks for model-serving infrastructure, covering drift, latency SLOs, error rates, and infra health
Cross-team leadership
Partner with Data Science to bring new models to production on a platform built to support them
Partner with the Web Application team where inference results power customer-facing features, keeping latency and reliability expectations aligned
Serve as the technical authority on MLOps practices as the Data Inference AI Pipeline team grows
What success looks like in your first 6 months:
Model deployment is a repeatable, automated process across models
Inference infrastructure has proven it scales up and down smoothly under real load
Cost-per-inference is measured, visible, and trending down as volume grows
A tested failover and disaster-recovery plan is in place for the inference pipeline, with at least one failure-injection exercise completed
Data Science can ship new models into production quickly and independently
What we’re looking for:
Minimum Qualifications
7-10+ years in software/ML engineering, including 3+ years focused specifically on MLOps or ML infrastructure