Software Engineer, Salesforce

• A related technical degree, or equivalent practical experience, required
• 5+ years of backend software development experience
• Deep knowledge of object-oriented programming languages: Java, C++, Go, C#, Ruby, Scala, Python, Node.js, or JavaScript
• Broad knowledge of web standards relating to APIs such as REST, SOAP, XML, WSDL, HTTP, JSON, etc
• Experience with Spring, gRPC, REST, and the HTTP protocol
• Experience with Agile development methodology
• 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
• Willingness to participate in the team's on-call rotation to address complex problems in real time and keep services operational and highly available

Desirable

• Design, build, test, and operate capabilities across the Momentum product, from meeting capture and recording through transcript processing, insight generation, and customer-facing experiences
• Develop robust backend services, APIs, and event-driven workflows that perform reliably at enterprise scale in a multi-tenant environment
• Contribute to frontend experiences using modern JavaScript and component-based frameworks, including Lightning Web Components where applicable
• Process and manage high-volume conversation, recording, transcript, and insight data securely and efficiently
• Design for security, privacy, compliance, data residency, observability, and operational readiness from the beginning
• Apply test-driven development practices and contribute to unit, integration, functional, performance, and end-to-end test coverage
• Use CI/CD tooling and modern engineering practices to safely deliver production-grade software
• Monitor production behavior, diagnose complex distributed-system failures, and improve system reliability and supportability
• Participate in architecture and code reviews, critically evaluating human- and AI-generated code for correctness, maintainability, security, accessibility, and performance
• Use AI development tools such as Claude Code, GitHub Copilot, Codex, or Cursor to improve development velocity, testing, debugging, and documentation
• Create clear system context, including architecture decisions, operational constraints, coding standards, and runbooks, so both engineers and AI-assisted workflows can operate effectively
• Collaborate with Product, Design, Security, Privacy, Site Reliability, and partner engineering teams to deliver trusted customer outcomes