Software Engineer (Multiple Levels) - Machine Learning Infrastructure, Slack

Software Engineer Role at Salesforce

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Ready to level-up your career at the company leading workforce transformation in the agentic era? Agentforce is the future of AI, and you are the future of Salesforce.

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.

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 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.

We are looking for Software Engineers to join the ML Infrastructure focus area and help architect and operate the core systems that power AI at Slack. In this role, you will own foundational infrastructure for large scale model training and inference, and evolve it into a reliable, secure, and self service platform used across the company.

You will work at the intersection of distributed systems, GPU infrastructure, and modern ML stacks, solving complex scalability and reliability challenges. This role blends deep systems engineering with a strong understanding of the ML lifecycle, and plays a critical part in shaping the long term technical foundations of Slack's AI capabilities.

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.