Lead Software Engineer, Knowledge & AI Platform

Salesforce Salesforce · Enterprise · San Francisco, CA +1

Lead Software Engineer for Salesforce's Knowledge & AI Platform, focusing on architectural evolution and end-to-end delivery of core Knowledge features. The role involves designing scalable systems, integrating with AI platforms like Einstein AI and Agentforce, and providing technical leadership and mentorship. While not directly building AI models, the role is central to integrating and scaling AI-driven features within the Salesforce ecosystem.

What you'd actually do

  1. Drive the architectural evolution and end-to-end delivery of core Knowledge features, including Self-Learning Knowledge (SLK) refactoring, SLK setup, and deep integrations with Data Cloud.
  2. Design scalable foundational systems — such as Data Cloud orchestration frameworks and event-driven architectures — that can be adopted across multiple engineering organizations.
  3. Take full ownership of product stability and customer success, leading critical production escalations, root cause analyses (RCAs), and resolving long-standing technical debt to unlock meaningful customer value.
  4. Multiply the team's impact through technical coaching and mentorship of engineers at all levels (AMTS, MTS, SMTS), fostering a culture of high-standard code reviews and engineering excellence.
  5. Champion Agile practices and continuous improvement, guiding the team toward clear milestones and technically sound incremental delivery in ambiguous environments.

Skills

Required

  • 8+ years of software engineering experience
  • deep expertise in Java
  • object-oriented design patterns
  • modern front-end development using JavaScript (ES6+), React
  • React Hooks
  • Next.js
  • Redux
  • web services
  • technical coaching and mentorship
  • Agile practices
  • service-oriented design
  • documenting and testing code

Nice to have

  • Exposure to AI/ML technologies, such as large language models (LLMs), recommendation systems, or similar.
  • Experience with cloud-native and microservices architectures
  • Spring Boot
  • Guice
  • message queues
  • event-driven systems

What the JD emphasized

  • deep expertise in Java
  • modern front-end development using JavaScript (ES6+), React
  • Experience building web-based solutions using web services

Other signals

  • architectural direction
  • integrations with Data Cloud, Einstein AI, and Agentforce
  • modernize the Knowledge experience
  • scalable foundational systems
  • technical coaching and mentorship