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Salary: USD 230,000 - 270,000 / annual
AiDASH is a satellite and AI-powered platform transforming grid resilience for electric utilities through vegetation, asset, storm, and wildfire intelligence. The company serves over 200 customers and is recognized as one of America's Best Startup Employers (Forbes, 4 consecutive years) and a top GreenTech company (TIME). AiDASH is pending acquisition by Schneider Electric.
You will be the founding Staff MLOps Engineer for a brand-new Data Inference AI Pipeline team, architecting the operational backbone from the ground up. This is a high-leverage individual contributor role where you'll shape deployment patterns, infrastructure standards, and engineering culture that future teammates will inherit. You'll report to the Director of Engineering for the Data Inference AI Pipeline team.
Key responsibilities span five areas:
**Deployment:** Own the end-to-end model deployment pipeline including packaging, versioning, rollout, and rollback. Build and maintain deployment infrastructure on AWS SageMaker (endpoints, batch transform, multi-model/multi-container hosting), and evaluate alternatives like self-hosted serving. Establish CI/CD patterns for model releases with 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) for each model and workload to balance latency and cost. Load-test the pipeline and build capacity plans aligned with 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. Define and test disaster-recovery procedures with regular failure-injection and chaos exercises. Build monitoring, alerting, and on-call runbooks covering drift, latency SLOs, error rates, and infrastructure health.
**Cross-team Leadership:** Partner with Data Science to bring new models to production on a platform built to support them. Align with the Web Application team on latency and reliability expectations. Serve as the technical authority on MLOps practices as the team grows.
Success in the first 6 months means: model deployment is a repeatable, automated process; inference infrastructure scales smoothly under real load; cost-per-inference is measured and trending down; a tested failover and disaster-recovery plan is in place with at least one failure-injection exercise completed.
Location: Hybrid role based in Palo Alto, CA, requiring 2 days per week in the office.
**Requirements:**
The posting does not explicitly state years of experience or specific technical certifications required. However, the role expects deep expertise in MLOps, model deployment pipelines, AWS SageMaker, CI/CD for ML, infrastructure-as-code, cost optimization, and distributed systems resilience. Experience designing and operating production inference infrastructure at scale is essential. Familiarity with monitoring, observability, and on-call practices for mission-critical systems is expected.
