Software Engineer, Salesforce

• You’ve led the architecture and delivery of complex backend systems through deployment, production failures, and subsequent improvements, with impact across multiple components or teams
• You have hands-on experience shipping and operating LLM-powered systems, and can explain how evaluations and production failures informed your design decisions
• You can reason through concurrency, persistent state, retries, and partial failures in systems that perform consequential actions
• You’re proficient in Python, Go, Java, or JavaScript/TypeScript
• You can turn an unclear customer problem into a bounded technical approach, test it with users, and lead it through production while creating capabilities other engineers can build on
• You want direct customer engagement to be part of your engineering work, including investigating problems and discussing technical tradeoffs
• You use AI development tools thoughtfully and take responsibility for the correctness, security, and maintainability of what you ship

Desirable

• Join a team of 10 within Salesforce AI Labs, bringing computer-use AI into production with the autonomy of a small startup and the reach of Salesforce. We’re building Systems Integration Agent, announced at Dreamforce, which learns how to operate enterprise applications and turns complex workflows into reusable, code-backed automation. Customers are already running workflows in production; now we’re making that success repeatable across more systems and use cases
• Making computer use dependable raises hard engineering questions. How does an agent know it succeeded? When should it reason, and when should it follow a deterministic path? How should it recover when an application changes or ask for help before making a consequential mistake?
• You’ll help answer those questions in production. Our team works directly with customers, building alongside them and turning discoveries into reusable product capabilities. As a Principal Engineer, you’ll remain hands-on while shaping architecture across our agent and backend systems, owning critical capabilities from design through operation, and helping other engineers deliver them
• Design and build systems that learn from demonstrations and combine AI reasoning with deterministic execution to complete workflows reliably
• Develop recovery and verification capabilities so agents can adapt to changes, confirm outcomes, and recognize when human intervention is needed
• Build backend services with robust workflow state management, failure handling, and secure access to customer environments
• Create evaluations and diagnostics that measure task success, expose regressions, and make failures understandable
• Work alongside customers to understand their workflows, prototype solutions, and validate results in their environments
• Investigate where automation succeeds or fails, then turn those findings into improvements to the core product
• Help customers move from their first successful workflow toward repeatable, increasingly self-service adoption
• Partner with forward-deployed engineers and product management to decide which customer needs should shape the roadmap
• Lead architectural decisions across agent execution, backend services, and workflow state, aligning system boundaries and tradeoffs with other technical leads
• Evaluate model selection, tool use, and context management against reliability, latency, and cost
• Raise the engineering bar through hands-on implementation, design reviews, and mentoring; help engineers take on broader ownership and participate in hiring