Software Engineer, AI Systems (PMTS)
Role Overview
Join a 10-person Salesforce AI Labs team bringing computer-use AI into production.
Build Systems Integration Agent, which learns to operate enterprise applications and turns complex workflows into reusable, code-backed automation.
Work directly with customers while shaping architecture across agent and backend systems; own critical capabilities from design through operation.
Key Skills for This Role
Full Job Posting
Role overview
Join a 10-person Salesforce AI Labs team bringing computer-use AI into production.
Build Systems Integration Agent, which learns to operate enterprise applications and turns complex workflows into reusable, code-backed automation.
Work directly with customers while shaping architecture across agent and backend systems; own critical capabilities from design through operation.
Agent development and architecture
- Design systems that learn from demonstrations and combine AI reasoning with deterministic execution.
- Build recovery and verification so agents adapt to changes, confirm outcomes, and request human intervention when needed.
- Develop backend services with workflow state management, failure handling, and secure customer-environment access.
- Create evaluations and diagnostics to measure success, surface regressions, and explain failures.
Customer collaboration and technical direction
- Prototype and validate customer workflows, investigate automation results, and turn findings into reusable product improvements.
- Help customers move toward repeatable, self-service adoption; work with forward-deployed engineering and product management on roadmap priorities.
- Lead architecture across agent execution, backend services, and workflow state; assess models, tools, and context management for reliability, latency, and cost.
- Contribute through implementation, design reviews, mentoring, and hiring.
Required qualifications
- Led architecture and delivery of complex backend systems through production deployment and improvement, with impact across components or teams.
- Hands-on experience shipping and operating LLM-powered systems and using evaluations and production failures to inform design.
- Can reason about concurrency, persistent state, retries, and partial failures in systems taking consequential actions.
- Proficient in Python, Go, Java, or JavaScript/TypeScript.
- Can turn unclear customer problems into bounded technical approaches, test with users, and lead work through production.
- Interested in direct customer engagement and accountable for the correctness, security, and maintainability of shipped work.
Helpful experience
- Agent tool use, code generation, automated recovery, or evaluation frameworks are helpful.
- Browser automation, workflow orchestration, enterprise integrations, early-stage product building, or customer-facing engineering are helpful.
Compensation
- Typical base salary is $172,500–$313,700 annually; select San Francisco and New York metropolitan areas have a range of $207,800–$344,700 annually.
- Ranges are base salary only and exclude applicable bonuses, sales incentives, equity, and benefits.
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