Software Engineer; Levels - Machine Learning Infrastructure, Slack

Software Engineer – ML Infrastructure

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.

• Design, build, and operate systems to train, serve, and deploy machine learning models at scale, focusing 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 Kube Ray, 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.
• 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, with expert‑level knowledge of Kubernetes and container‑based platforms.
• Hands‑on experience with modern ML infrastructure and serving stacks such as Ray, Kube Ray, vLLM, or similar.
• 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.
• Demonstrated ability to drive technical direction for complex systems and balance short‑term delivery with long‑term architectural goals.
• Excellent written communication and ability to thrive in an asynchronous and globally distributed infrastructure team.
• A related technical degree is required.
• Demonstrated, genuine AI‑first approach to engineering, using AI to move faster and contribute well beyond core specialty.
• Experience using AI tools (e.g., Claude Code, Git Hub Copilot, Codex, Cursor) in development workflows.
• Advanced prompt engineering skills and ability to write precise, structured prompts that cultivate system context for reliable, secure, production‑ready AI outputs.

Salesforce offers a comprehensive benefits package including medical, dental, vision, mental‑health support, paid parental leave, life and disability insurance, 401(k), and employee stock purchasing program. Salary for this role ranges from $148,500 to $313,700 annually, excluding additional bonuses, equity, or other incentives. More details are available at

Salesforce is an equal‑opportunity employer and maintains a policy of non‑discrimination with all employees and applicants for employment.