Software Engineering, AI Systems (PMTS)

About the Role

Salesforce is seeking a Principal Engineer to join the Salesforce AI Labs team, focusing on bringing computer-use AI into production. This role operates with the autonomy of a small startup and the reach of Salesforce, building the Systems Integration Agent that learns to operate enterprise applications and automate complex workflows. The team works directly with customers, turning discoveries into reusable product capabilities.

Key Responsibilities

• Drive Agent Development & Architecture:
• Design and build systems that learn from demonstrations and combine AI reasoning with deterministic execution to complete workflows reliably.
• Develop recovery and verification capabilities for agents to 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 to measure task success, expose regressions, and make failures understandable.
• Build Directly With Customers:
• 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.
• Shape Technical Direction & Grow the Team:
• Lead architectural decisions across agent execution, backend services, and workflow state, aligning system boundaries and tradeoffs.
• 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.

Requirements & Tech Stack

• Experience leading the architecture and delivery of complex backend systems through deployment, production failures, and subsequent improvements, with impact across multiple components or teams.
• Hands-on experience shipping and operating LLM-powered systems, and can explain how evaluations and production failures informed design decisions.
• Ability to reason through concurrency, persistent state, retries, and partial failures in systems that perform consequential actions.
• Proficiency in Python, Go, Java, or JavaScript/TypeScript.
• Ability to 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.
• Direct customer engagement is a key part of the engineering work, including investigating problems and discussing technical tradeoffs.
• Thoughtful use of AI development tools and taking responsibility for the correctness, security, and maintainability of shipped code.

Qualifications (Even Better If)

• Experience with agent tool use, code generation, automated recovery, or evaluation frameworks.
• Experience with browser automation, workflow orchestration, or enterprise integrations.
• Experience building an early-stage product or working in a customer-facing engineering role.