Software Engineer (SWE/SWE II), AI Platform- Slack

Salesforce · Seattle, Washington

About Slack AI

Slack AI's mission is to transform how people work by making Slack an AI-powered operating system. We're tackling significant challenges like unlocking collective knowledge and reducing noise, all while building a seamless, consumer-grade AI experience within users' existing workflows. Join us in shaping the future of work through AI.

The software engineer role at Salesforce encompasses architecture, design, implementation, and testing to ensure we build products right and release them with high quality. Equally important is advanced prompt engineering — the ability to write precise, structured prompts and cultivate the system context that makes AI outputs reliable, secure, and production-ready.

About the Team

The AI and ML Infrastructure team is part of Slack’s Core Infrastructure organization and is responsible for the foundational systems that enable machine learning and AI across the company. The team designs, builds, and operates reliable, scalable, and high performance platforms that allow product and ML teams to develop, deploy, and operate AI driven capabilities with confidence.

The team owns shared infrastructure, services, and tooling that support the full ML lifecycle, including model training, deployment, inference, and monitoring. As Slack AI continues to grow, the team is evolving from traditional ML deployments toward large scale, highly distributed model systems. This work involves deep architectural decisions around scalable model deployment strategies, real time feature serving at very high throughput, GPU accelerated inference at message scale, and responsible training of models on sensitive data with strong privacy and safety requirements.

Core Focus Areas

  • ML Infrastructure - The ML Infrastructure focus area is responsible for the low level systems that power training and inference at scale. This includes architecting and maintaining distributed systems for model training, serving, and deployment using Kubernetes based platforms, GPU infrastructure, and open source ML stacks such as KubeRay and vLLM. The team delivers platform capabilities that improve the speed, reliability, and quality of ML development, including training pipelines, feature generation systems, and compute orchestration.

  • AI Platform - The AI Platform focus area builds the tooling and platform layers that enable AI development across Slack. This includes creating developer facing tools, SDKs, and workflows that allow product teams to integrate AI into Slack features efficiently and safely. The platform supports LLM efficiency and model transition initiatives through integrations with managed services across multiple cloud providers acting as the connective layer between core infrastructure and product engineering teams.

About the Role

We are looking for Software Engineers to join the AI Platform effort and build the developer experience that powers AI at Slack. In this role, you will treat internal engineers as your primary customers, designing and building tooling, SDKs, and evaluation frameworks that enable product teams to ship AI features faster and more reliably.

You will work closely with ML Infrastructure, modeling, and product teams to make informed decisions around open source versus managed solutions, improve the usability and reliability of our AI platforms, and accelerate the adoption of AI across Slack.

What you’ll be doing

  • Drive the evolution of Slack’s AI and ML platform toward a self service, developer friendly environment that improves velocity and reliability

  • Build and maintain SDKs, feature generation tools, and CI CD pipelines that make it easy for product teams to integrate AI into their workflows

  • Manage and evolve integrations with managed AI services across multiple cloud providers

  • Design and operate AI quality evaluation frameworks and prompt engineering infrastructure to ensure AI features meet high standards for reliability and user experience

  • Collaborate closely with ML modeling, AI quality, and product engineering teams to design platforms that meet evolving technical and business needs

  • Build and ship high-quality, production-grade software using modern engineering practices, with AI as a core part of your development workflow by pushing the boundaries of AI development tools to deliver secure, optimized, and high-quality code.

  • Design and orchestrate complex systems where AI agents integrate seamlessly into human workflows, driving efficiency and innovation at scale.

  • Contribute to building and maintaining the shared system context, an explicit repository of system designs, constraints, and standards that enables AI to operate accurately and reliably.

  • Critically evaluate code (Human or AI-generated) for correctness, quality, security, and performance

What you should have

  • Experience building developer tooling, libraries, or CI CD pipelines that improve engineering speed, quality, and usability

  • Experience operationalizing Large Language Models (LLMs) and building integrations with first party APIs and external cloud provider APIs such as AWS, GCP, or Azure

  • Experience with AI quality evaluation frameworks, prompt engineering infrastructure, or developer tooling for ML workflows

  • Strong proficiency in Python, PHP or Hacklang and experience with infrastructure as code and modern software engineering practices

  • Ability to communicate complex technical concepts clearly and effectively to a broad range of stakeholders

  • Love to model modern methodologies for unit tests, code review, design documentation, debugging, and troubleshooting.

  • Are curious, inquisitive, and determined to fix things when they break.

  • Work well with a team of diverse backgrounds and experience on complicated projects.

  • A related technical degree required

  • A demonstrated, genuine AI-first approach to engineering. Using AI to move faster, build fluency across the stack, and contribute well beyond your core specialty.

  • Experience using AI tools (e.g., Claude Code, GitHub Copilot, Codex, Cursor, etc.) in development workflows

  • Advanced prompt engineering skills and the ability to write precise, structured prompts and cultivate the system context that makes AI outputs reliable, secure, and production-ready.

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