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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Software Engineer, Site Reliability - **Company:** Salesforce Inc. - **Location:** Boston, MA, United States - **Experience:** Expert - **Salary:** $148,500.0 - $223,900.0 - **Contract:** Permanent contract - **Skills:** OpenTofu, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Software as a Service, Databases, Software Debugging, Programming Tools, Distributed Systems, Python (Programming Language), PostgreSQL, Performance Tuning, Query Optimization, Reliability Engineering, Software Engineering, TypeScript, Sql Optimization, Istio, Claude Code, Technical Debt, Indexer, Agentic-AI, Kubernetes, Information Technology, Machine Learning Operations, Terraform, AIOps, Data Pipelines, Golang, Microservices - **Published:** October 31, 2026 - **Apply:** https://www.themuse.com/jobs/salesforce/senior-software-engineer-site-reliability-agentforce-ops ## About the Role * 5+ years of experience in SRE, Production Engineering, or Backend Engineering with a heavy focus on operations and scale. * Proven Scaling Experience: You have previously helped take a product through a high-growth phase (the "1 to 100" journey), dealing with the technical debt and architectural shifts that come with it. * Coding Mastery: Ability to write and review production-level code in Golang, TypeScript, or Python-you view automation as a software engineering problem. * Technical Breadth: Strong proficiency in Kubernetes, Terraform/OpenTofu, and AWS/GCP/Azure. * Observability Expert: Demonstrated experience with properly instrumenting applications and platforms with SLO-based monitoring and alerting. * Systems Expert: Deep understanding of distributed systems, including how to debug complex interactions between microservices, databases, and AI agents. * Low-Ego Collaboration: Experience working within a senior team of Principal engineers, capable of both leading specific initiatives and supporting the broader group's technical vision. * AI Fluency: A demonstrated ability to use modern AI development tools to move faster and build more reliable systems. * Ownership Mentality. We are a senior team who operate with a high degree of autonomy. As a result, we're looking for someone who can independently identify issues and own the solution all the way through final implementation. Even Better If... * M.S. in Computer Science or equivalent practical experience. * Database Specialist: Strong experience with PostgreSQL at scale (partitioning, indexing, query tuning). * Distributed Systems: Advanced knowledge of microservice orchestration and durability patterns, including hands-on experience with Temporal for workflow reliability and Istio service meshes for secure, observable service-to-service communication in high-growth SaaS environments. ## Description As a Production Support Engineer (SMTS), you will be a senior technical member of staff within our embedded reliability team. You aren't building the foundation alone-you'll work alongside a group of engineers and product owners to ensure the Agentforce Operations platform is the most reliable AI-powered engine in the industry. This is a role for an engineer who loves the "scaling" problem. You will focus on production excellence, performance tuning, and infrastructure automation. Because you are embedded in the product organization, you'll have a seat at the table during design reviews, ensuring that as we add new agentic capabilities, they are built to scale from day one. * Scaling & Reliability: Own the reliability roadmap for major product areas, working to transition our systems from startup-speed architectures to highly-available, global-scale enterprise solutions. * Collaborative Leadership: Partner with PMTS-level engineers to refine our infrastructure strategy, contributing senior-level perspectives on system design, capacity planning, and bottleneck identification. * Infrastructure as Code: Maintain and evolve our automated environments, focusing on making our "infrastructure-as-plugins" model more robust and developer-friendly. * AI Operations (AIOps): Support the scaling of our AI/ML infrastructure, ensuring our models have the GPU resources and data pipelines required to deliver real-time supply chain insights. * Production Excellence: Lead the "1 to 100" hardening of our observability stack. You won't just respond to incidents; you'll build the tooling that prevents them and the telemetry that explains them. * Performance Engineering: Deep-dive into SQL optimization, API latency, and cross-service communication to ensure our data-intensive supply chain platform remains performant under heavy load. * AI-First Workflow: Lean into the future of engineering by using AI tools (Claude Code, Codex, etc.) to automate routine operational tasks and accelerate infrastructure delivery.