Staff Engineer, Customer Insights

Together AI Together AI · Data AI · San Francisco, CA · Engineering

Staff Engineer to build and scale the customer-facing visibility layer for Together's AI Cloud, focusing on historical analytics, activity history, audit logs, event timelines, notifications, and investigation workflows. The role will evolve these foundations into AI-first investigation and insight workflows that summarize activity, explain anomalies, and provide trustworthy context for human operators and autonomous agents. This is a hands-on role designing event, query, delivery, and governance systems, and building user-facing workflows for enterprise customers.

What you'd actually do

  1. Design and drive the architecture for Together's Customer Insights platform, including customer-visible events, activity history, audit logs, timelines, notifications, and historical analytics and dashboards views.
  2. Build and operate the shared event and historical analytics visibility layer that helps product teams define important customer-facing events once and surface them consistently across analytics, logs, timelines, notifications, and governance views.
  3. Write and maintain critical-path backend and product code used by multiple teams and customer-facing surfaces.
  4. Partner with Data Platform and Observability engineering teams to leverage Together’s core data stack and capabilities to build out Customers Insights data collection and processing systems.
  5. Create customer workflows that connect summaries, timelines, evidence, actors, related objects, and next steps so users can investigate operational questions without relying on support or ad hoc analysis.

Skills

Required

  • 8+ years of experience building and operating large-scale distributed systems, product platforms, or customer-facing backend systems in production environments.
  • Proven experience designing platforms or product systems used by multiple engineering teams or enterprise customers.
  • Strong backend engineering skills in one or more of TypeScript, Go, Python, Java, C++, or similar production languages.
  • Deep experience with event-driven systems, audit trails, activity feeds, analytics platforms, observability systems, notification systems, or investigation workflows.
  • Strong data modeling and systems design judgment, including schemas, retention, indexing, query performance, access control, and correctness tradeoffs.
  • Demonstrated ability to turn ambiguous customer and operational needs into clear technical architecture, incremental delivery plans, and durable product foundations.
  • Experience partnering across product, infrastructure, data, security, and customer-facing teams to land cross-cutting architecture without relying on formal authority.
  • Comfort writing critical production code while also setting technical direction, mentoring senior engineers, and raising engineering standards.

Nice to have

  • Experience building enterprise SaaS audit logs, governance tooling, compliance evidence systems, data lineage, or incident investigation products.
  • Experience with high-volume event pipelines, streaming systems, OLAP or time-series storage, search systems, or large-scale analytical query engines.
  • Experience building notification, alert routing, escalation, or workflow automation systems.
  • Experience designing customer-facing analytics, reporting, dashboards, or operational intelligence products.
  • Experience with multi-tenant cloud platforms, identity-aware access patterns, sensitive data handling, and retention controls.
  • Experience with AI infrastructure, developer platforms, observability, or systems that support mission-critical enterprise workloads.
  • Experience running production workloads on Kubernetes or cloud infrastructure and using Infrastructure as Code and modern CI/CD workflows.
  • Experience building product surfaces in addition to backend platforms, especially where UX quality and technical correctness both matter.

What the JD emphasized

  • customer-facing visibility layer
  • AI-first investigation and insight workflows
  • audit logs
  • governance systems
  • investigation workflows
  • critical-path backend and product code
  • customer workflows