Software Engineer (SWE/SWE II), AI Platform- Slack
Salesforce - Washington - Seattle; Texas - Austin; Georgia - Atlanta; Washington - Bellevue
Posted Apr 27, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Verification
- Source-linked last checked May 7, 2026
- Salary
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- 401(k) match
- Listed Source: EMPLR_CONTRIB_INCOME_AMT. source Last checked May 7, 2026.
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Schedule
- Shift type
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- Weekend work
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Application
- Cover letter
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- Assessment
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- Deadline
- Not stated
Where they hire
State eligibility is not yet verified.
About this role
Software Engineer (SWE/SWE II), AI Platform- Slack Washington - Seattle; Texas - Austin; Georgia - Atlanta; Washington - Bellevue 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
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