Principal Software Engineer, Backend (Connectivity)
$197,300–$313,700 year
HybridSan Francisco, California, United States
Job Summary
Lead design and development of core integration capabilities and scalable connectivity solutions across MuleSoft, Salesforce, and Agentforce. Collaborate with cross-functional teams to define requirements and translate them into technical solutions. Set technical direction, shape long-term strategy, and provide mentorship to engineering teams while promoting best practices in coding, design, and architecture. Drive innovation through new ideas, improved processes, and bold experimentation. This role blends technical depth with strategic thinking for a team building AI-powered systems from the ground up. The position supports autonomous teams delivering meaningful value to customers in the agentic era.
Required Qualifications
- 8+ years of experience building and operating production-grade software systems
- Strong proficiency in Java and Spring Boot, or in comparable languages and frameworks for building scalable backend systems
- Hands-on experience designing and building APIs, asynchronous workflows, and event-driven systems
- Experience working with large codebases and influencing design decisions across teams
- Ability to think holistically about architecture, performance, and operational stability
- Strong troubleshooting skills with the ability to triage complex issues and identify opportunities to improve product quality and reliability
- Ability to communicate complex ideas clearly and work effectively with engineers, designers, and product teams
Desired Qualifications
- Hands-on experience with MuleSoft platform (Anypoint Studio, Mule Runtime, Anypoint Connectors)
- Expertise in Salesforce platform, including Apex, Lightning Components, and Salesforce APIs
- Solid experience with system performance tuning, memory management, and optimizing resource usage in distributed environments
- Familiarity with container runtimes such as Docker and orchestration tools like Kubernetes
- Familiarity with large language models (LLMs), RAG architecture, and integration of AI into developer workflows
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