Software Engineer, AI Systems (PMTS)

Salesforce•Boston, MA

3d•$172,500 - $344,700•Hybrid

About The Position

Salesforce is seeking a Software Engineer for its AI Systems team within Salesforce AI Labs. This role involves bringing computer-use AI into production, operating with the agility of a startup while leveraging the resources of Salesforce. The team is focused on building a Systems Integration Agent that learns to operate enterprise applications and automates complex workflows. The position offers the opportunity to work directly with customers, turning discoveries into reusable product capabilities and shaping the future of AI in customer success.

Requirements

Led 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.
Can reason through concurrency, persistent state, retries, and partial failures in systems that perform consequential actions.
Proficient in Python, Go, Java, or JavaScript/TypeScript.
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.
Direct customer engagement is part of engineering work, including investigating problems and discussing technical tradeoffs.
Use AI development tools thoughtfully and take responsibility for the correctness, security, and maintainability of what is shipped.

Nice To Haves

Agent tool use, code generation, automated recovery, or evaluation frameworks.
Browser automation, workflow orchestration, or enterprise integrations.
Building an early-stage product or working in a customer-facing engineering role.

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 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.
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 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.