MLOps Engineer

AiDASH is leading the Prevention First movement for electric utilities and transforming grid resilience through its pioneering platform that unifies vegetation, asset, storm, and wildfire intelligence. Powered by Satellite First 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.

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The Prevention First 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 Green Tech 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!

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 '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.

This is a hybrid role based in Palo Alto, CA, requiring 2 days per week in the office.

• 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 Sage Maker (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

• 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

• 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
• 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

• 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

• 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…