Software Engineering, Salesforce

• You’ve independently owned a substantial backend service or system through design, deployment, production failures, and subsequent improvements
• You have hands-on experience building and evaluating LLM-powered systems, and can explain how you assessed their quality and failure modes
• 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 carry it through to production
• 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 a product that learns from human demonstrations and turns complex workflows into repeatable automation across enterprise applications
• 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. You’ll own substantial systems from design through deployment and help take a new offering from early customer adoption through general availability, backed by Salesforce’s scale and reach
• 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
• Own technical decisions for substantial components, from initial design through production operation
• Evaluate model selection, tool use, and context management against reliability, latency, and cost
• Mentor peers through design discussions and code reviews, and participate in hiring