Software Engineer, Customer Insights

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

Software Engineer for Customer Insights team at Together AI, focusing on building and operating customer-facing visibility layers for AI workloads. This role involves developing features for analytics, activity history, audit logs, and investigation workflows, partnering with Data Platform and Observability teams. The position requires backend engineering skills and experience with large-scale distributed systems, with an emphasis on learning event-driven systems and analytics pipelines.

What you'd actually do

  1. Build and ship features for Together's Customer Insights platform, including customer-visible events, activity history, audit logs, timelines, notifications, and historical analytics and dashboards views.
  2. 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.
  3. Own well-defined pieces of work end-to-end, from implementation through testing and rollout
  4. Write and maintain critical-path backend and product code used by multiple teams and customer-facing surfaces.
  5. Surface blockers early and collaborate with the team to work through them

Skills

Required

  • 2-5 years of experience building and operating large-scale distributed systems, product platforms, or customer-facing backend systems in production environments.
  • Good backend engineering skills in one or more of TypeScript, Go, Python, Java, C++, or similar production languages.
  • Genuine enthusiasm to learn event-driven systems, analytics pipelines, and the customer visibility space
  • Developing data modeling instincts - comfort working with schemas, queries, and relational and/or non-relational data
  • Writes clean, well-organized, well-tested code
  • Comfortable owning scoped work and surfacing blockers early
  • Curious, open to feedback, and willing to propose new approaches and make mistakes

Nice to have

  • Interest in, or some exposure to, stream processing, data pipelines, notification systems, or analytical systems

What the JD emphasized

  • customer-facing visibility layer
  • historical analytics
  • activity history
  • audit logs
  • event timelines
  • notifications
  • investigation workflows
  • autonomous agents