Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Lead engineering teams to deliver a cloud-native SaaS platform and ship production AI agents for MEP fabrication workflows. Own delivery, architecture (Azure/MongoDB Atlas), CI/CD, observability, and cross-functional execution. Maintain customer empathy via shop-floor engagement, partner with Product/CTO, and build an AI-forward engineering culture and high-performing teams.

The Solutions Engineer - Electrical Specialist leads onboarding, technical deployment, and implementation for electrical contractors, ensuring success through configuration and integration with Stratus.

Manage a portfolio of 30–50 commercial mid-market SaaS accounts to drive adoption, retention, renewals, and expansion. Build executive relationships, deliver QBRs, forecast renewals in CRM, identify upsell opportunities, partner cross-functionally on onboarding and product feedback, and travel ~25–30% working US Eastern time.

Full-cycle, quota-carrying AE owning territory end-to-end: self-source pipeline, run discovery, build ROI business cases, partner on demos, and close $30K-$100K+ deals to hit ~ $1M quota. Maintain Salesforce, move fast on ~45-day cycles, and travel ~40%.

Own the electrical product portfolio across Stratus and Works, delivering BIM/CAD interoperability and electrical prefab-to-field fabrication workflows. Define PRDs/specs for CAD and fabrication edge cases, coordinate integrations with Autodesk, engage customers and site visits, track adoption, and identify AI opportunities to accelerate CAD/fabrication tasks.

Own and scale a B2B SaaS demand generation engine across paid, organic, ABM, email/lifecycle, and events. Build multi-channel programs from awareness through MQL/SQL handoff, partner with sales/RevOps on ICP and routing, optimize paid media for CAC and pipeline efficiency, and establish reporting cadence. Continuously test, iterate, and build scalable systems and processes to drive measurable pipeline and conversion improvement.

Own and implement the canonical data model and governance for a multi-tenant MEP SaaS platform. Architect data for AI/ML readiness (RAG, embeddings, vector search), design polyglot persistence and lake/lakehouse pipelines, lead staged modernization and migrations, produce hands-on prototypes and in-repo guardrails, and partner with platform, DB engineering, and ML teams to ensure data quality, lineage, and observability.