Principal Member of Technical Staff (PMTS) / Staff Software Engineer – AI Platform (Agentic Systems)

Salesforce•San Francisco, CA

About The Position

Salesforce is hiring a Principal Member of Technical Staff (PMTS) / Staff Software Engineer for Slack’s Agentic Operations team. The team builds and operates platforms and internal “software factories” that automate how Slack’s engineering organization develops, operates, and maintains its services. Its goal is to turn repetitive, high-volume engineering work into reliable, supervised autonomous workflows so engineers can focus on architecture and judgment. The role sets technical direction and architecture for how Slack builds, registers, and safely operates autonomous agentic systems. It leads cross-team initiatives to transform engineering workflows—including multi-repository code generation and automated incident remediation—into dependable, supervised, continuously improving pipelines at enterprise scale. The systems are built on Slack’s agentic development platform (Speck), its compute platform (EKS and EC2), and the service catalog, and establish patterns, guardrails, and operational standards for engineering teams adopting autonomous agents. The position covers autonomous code generation and remediation; operational and incident automation; evaluation, guardrails, safety, and observability; durable execution and state management, including human approval and rollback mechanisms; and agentic processes that maintain service catalog and entity metadata. Salesforce describes its benefits and resources as supporting balance and impact. The position is covered by Salesforce’s equal opportunity and non-discrimination policy.

Requirements

10+ years of software engineering experience designing and delivering complex distributed systems at scale, with Staff- or Principal-level technical judgment and organizational influence.
Hands-on experience building and shipping production LLM-powered agents and multi-agent systems, including orchestration, structured tool or function calling, retrieval-augmented generation (RAG), and context-window engineering.
Experience making non-deterministic AI systems production-ready through deterministic evaluation suites, regression harnesses, guardrails, and LLM telemetry.
Strong backend and distributed-systems engineering foundations, including clean, scalable, maintainable code and direct accountability for uptime, latency, and reliability.
A pragmatic systems-level automation mindset, comfort operating amid ambiguity, and ability to balance rapid AI capability development with production safety.
Experience building autonomous code-generation pipelines, automated refactoring systems, or software factories that open pull requests and land changes across monolithic or multi-repository architectures.
Experience designing agent evaluation frameworks, prompt or context management layers, or LLM observability platforms used by other engineering teams.

Nice To Haves

Deep background in service orchestration or automated incident remediation at scale.
Hands-on experience with AWS, Kubernetes (EKS), and Infrastructure as Code (Terraform), using reusable, maintainable abstractions.
Contributions to open-source agentic frameworks, developer tools, or the broader AI developer ecosystem.
Familiarity with frontier model APIs and agentic frameworks, such as Anthropic Claude Tool Use, OpenAI function calling, Model Context Protocol (MCP), and LangGraph, and practical understanding of their failure modes.

Responsibilities

Set technical strategy and architecture for Slack’s agentic operations platform, including standards for safely building, registering, and operating autonomous agents and software factories at scale.
Design end-to-end autonomous pipelines for code generation, bug fixing, and incident remediation, including orchestration, tool integrations, and human-in-the-loop controls.
Build evaluation suites, regression harnesses, guardrails, and observability frameworks to make non-deterministic agentic systems measurable, debuggable, and safe in production.
Establish ownership and accountability models for detecting, attributing, and remediating autonomous-system failures, including on-call policies and operational guardrails.
Partner with platform teams and engineering groups to integrate agentic workflows into daily developer routines.
Promote cost-conscious and efficient model and compute utilization, balancing model capability and latency against token budgets and compute costs.
Mentor senior engineers, lead architectural reviews, and write system designs that establish reusable technical patterns across the organization.