Internship Slack Software Engineer Jobs in Bothell, WA

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• 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.

• Design, build, and operate systems to train, serve, and deploy machine learning models at scale, with a focus on reliability, performance, and operational simplicity
• Evolve GPU backed inference infrastructure to support high throughput, latency sensitive workloads, including large scale model serving
• Architect and optimize distributed training and data processing systems using platforms such as Ray, Airflow, Spark, or similar technologies
• Build and maintain Kubernetes based platforms and orchestration layers using tools such as KubeRay, vLLM, and internally developed services
• Architect solutions that bridge legacy systems with modern technologies while maintaining monolithic application stability
• Develop robust monitoring, observability, and alerting for production ML workloads to ensure operational excellence
• Partner closely with AI Platform, ML modeling, security, and product engineering teams to design infrastructure that supports evolving AI use cases
• Provide technical leadership through design reviews, mentorship, and by setting engineering standards and long term architectural direction for ML infrastructure
• Author technical design and architecture documentation, and contribute thought leadership through engineering blog posts
• 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

• Significant professional experience in software engineering with a strong focus on infrastructure, backend systems, platform engineering, or MLOps
• Deep experience building and operating distributed systems, including expert level knowledge of Kubernetes and container based platforms
• Hands on experience with modern ML infrastructure and serving stacks such as Ray or KubeRay, vLLM, or similar training and inference orchestration frameworks
• Experience working with GPU infrastructure, including performance optimization and operational management at scale
• Strong experience with data infrastructure and orchestration technologies such as Airflow, Spark, or similar systems
• Experience building and operating cloud native systems on public cloud platforms such as AWS, GCP, or Azure, including infrastructure as code
• A demonstrated ability to drive technical direction for complex systems and balance short term delivery with long term architectural goals
• Excellent written communication, as well as ability to thrive in an asynchronous and globally distributed infrastructure team.
• 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.

Unleash Your Potential

When you join Salesforce, you'll be limitless in all areas of your life. Our benefits and resources support you to find balance andbe your best, and our AI agents accelerate your impact so you cando your best. Together, we'll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love. Apply today to not only shape the future - but to redefine what's possible - for yourself, for AI, and the world.

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