Risely Ai

Risely Ai

Founding Platform Eng Lead

San Francisco, USA

Sponsorship not specified$150k-$215kDetected 72 days ago
TypeScriptPythonPostgreSQLSnowflakeAWSGCPKubernetesPlatform EngineeringRESTData EngineeringLLMsCybersecuritySalesforceHRIS

About the role

  • We're live at some of the largest universities in the country and growing fast.
  • This is a player-coach role at the foundation of the company.

Responsibilities

  • We're hiring our Founding Platform Lead.
  • You'll own and build the core platform that delivers end-to-end deployments to large universities at record speeds with excellent quality.
  • What you'll own The platform that delivers in 4 weeks.
  • Most companies in our space take 12 months to deliver an implementation.

Requirements

  • You know which patterns deserve to become primitives.
  • You know when to stop adding surface area and reinforce the platform underneath.
  • Experience 3-8 years of production engineering.
  • You have a position on shared versus isolated infrastructure and per-customer cost accounting.

Nice to have

  • the data layer against the customer's source systems, the agent configuration against their workflows, and the security and sync infrastructure each customer inherits.
  • Today, a complex deployment can take us 10-12 weeks to get done, down from 12 months.
  • Your job is to take it lower, and keep it there as the customer base grows from 10 to 40 to 400.
  • The deployment automation.
  • A pipeline of agents that does the work an engineering team would otherwise do manually for every customer.
  • The security platform.
  • Compliance, RBAC, encryption, audit trails, agent guardrails.
  • The connector and data engineering strategy.

Compensation

  • $150k-$215k

Benefits

  • $150-215K total cash (incl. variable) plus founding-level equity Risely is building the agentic operating system for universities.
  • The work, the pressure, and the equity are all founder-grade.

This listing is sourced directly from Risely Ai's careers page and normalized into a canonical job model.