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

Job Description

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts. Job Category Software Engineering Job Details About Salesforce Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all. Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce. 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 developmen

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