Software Engineering, AI Systems (PMTS)

Salesforce•Boston, MA

3d•$197,300 - $344,700

About The Position

Salesforce AI Labs is a 10-person team building the Systems Integration Agent, which learns to operate enterprise applications and turns complex workflows into reusable, code-backed automation. The product is already being used by customers in production, and the team is working to make successful workflows repeatable across more systems and use cases. The Principal Engineer will remain hands-on while shaping architecture across agent and backend systems, own critical capabilities from design through operation, and help other engineers deliver them. The role includes building dependable computer-use AI, combining reasoning with deterministic execution, and creating mechanisms to verify results, recover from changes, and request human intervention when appropriate. The engineer will work directly with customers to understand workflows, prototype and validate solutions, investigate failures, and translate findings into reusable product capabilities. They will also guide technical direction across agent execution, backend services, and workflow state; evaluate models and tools against reliability, latency, and cost; and raise the engineering bar through implementation, design reviews, mentoring, and hiring.

Requirements

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 ability to explain how evaluations and production failures informed design decisions.
Ability to reason about 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.
Willingness to engage directly with customers, investigate problems, and discuss technical tradeoffs.
Thoughtful use of AI development tools and accountability for the correctness, security, and maintainability of shipped work.

Nice To Haves

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.

Responsibilities

Design and build systems that learn from demonstrations and combine AI reasoning with deterministic execution to complete workflows reliably.
Develop recovery and verification capabilities that let agents 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.
Work alongside customers to understand workflows, prototype solutions, and validate results in their environments.
Investigate where automation succeeds or fails and turn findings into improvements to the core product.
Help customers progress from their first successful workflow toward repeatable, increasingly self-service adoption.
Partner with forward-deployed engineers and product management to determine 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, mentoring, and participation in hiring.