Senior / Lead Software Engineer, Full Stack (product)

Salesforce Salesforce · Enterprise · Washington - Seattle Metro -, Georgia - Atlanta Metro, CA · Remote

Salesforce is seeking a Senior / Lead Full Stack Software Engineer to build AI-powered experiences using LLMs and embeddings. The role involves shipping end-to-end product features, partnering with product and design, and ensuring AI features are observable, testable, and maintainable. The ideal candidate has experience applying AI/ML in products and a strong understanding of full-stack development.

What you'd actually do

  1. Ship full-stack product features end-to-end across frontend, backend, and data
  2. Build AI-powered experiences using LLMs, embeddings, and related techniques where they create real user value
  3. Own delivery from scope/design through launch, monitoring, and iteration
  4. Solve ambiguous, technically challenging problems with pragmatic, scalable solutions
  5. Partner closely with product, design, and engineering to shape requirements and roadmap

Skills

Required

  • 5+ years of experience in professional software development
  • Proven experience shipping production full-stack web applications
  • Hands-on experience applying AI/ML in product (LLMs, embeddings, or similar)
  • Comfortable operating in ambiguity and driving work forward independently
  • Product-minded: you care about UX and customer outcomes, not just implementation
  • Strong communicator and collaborator across functions
  • Ownership mindset and bias for action

Nice to have

  • Experience with modern web frameworks (Rails/React/Postgres/AWS is a plus; adaptability matters most)
  • Experience with enterprise SaaS (especially B2B marketing or sales tech)
  • Experience with real-time systems (chat, messaging, event-driven)
  • Familiarity with Salesforce/CRM integrations
  • Experience building agentic systems or autonomous tool-using workflows
  • Startup experience and rapid shipping

What the JD emphasized

  • AI-powered experiences using LLMs, embeddings, and related techniques where they create real user value
  • practical, reliable product experiences
  • observable, testable, and maintainable
  • Hands-on experience applying AI/ML in product (LLMs, embeddings, or similar)
  • AI as a core product surface
  • strong engineering around evaluation, observability, and iteration
  • building agentic systems or autonomous tool-using workflows

Other signals

  • Build AI-powered experiences using LLMs, embeddings, and related techniques where they create real user value
  • Help turn new AI capabilities into dependable experiences that customers trust, with strong engineering around evaluation, observability, and iteration