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
Benefits
Medical Care
Life Insurance
Retirement Savings
Employee Assistance Programs
With 9 standard holidays and four floating holidays, you get a total 13 paid days off each year- You use AI development tools thoughtfully and take responsibility for the correctness, security, and maintainability of what you ship
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 want direct customer engagement to be part of your engineering work, including investigating problems and discussing technical tradeoffs
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 can reason through concurrency, persistent state, retries, and partial failures in systems that perform consequential actions
Browser automation, workflow orchestration, or enterprise integrations
Agent tool use, code generation, automated recovery, or evaluation frameworks
Building an early-stage product or working in a customer-facing engineering role
