Why This Job is Featured on The SaaS Jobs
This Software Engineer role sits at the intersection of core SaaS infrastructure and the emerging shift toward agentic AI inside established, data-rich products. Within a communications SaaS context, the focus on autonomous workflow execution and real-time conversational reasoning reflects where many SaaS platforms are heading as they embed AI deeper than surface-level copilots.
For SaaS engineers, the career signal here is platform-building rather than feature delivery. Work on orchestration layers, memory and retrieval architectures, and evaluation and observability maps directly to the operational realities of running AI in production for enterprise customers. The cross-functional interface with product, applied research, design, and executive stakeholders also mirrors how mature SaaS organizations align technical bets with roadmap and risk.
This role best fits senior engineers who prefer owning architectural direction, setting technical standards, and mentoring peers while still staying close to implementation. It will suit professionals comfortable working across distributed systems and applied AI, especially those motivated by ambiguous problem spaces where reliability, safety guardrails, and measurable system behavior matter as much as model capability. On-site in San Francisco, it also favors candidates who value tight collaboration with leadership and partner teams.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
Your role
As a core technical leader within our Agentic AI initiatives, you will shape the vision, architecture, and execution of Dialpad’s next-generation AI platform, moving the industry beyond passive copilots into autonomous workflow execution. Working closely with Product, Applied Research, Design, and executive leadership, you will build production-grade systems where AI agents reason, act, coordinate, and safely execute workflows.
You will help pioneer an advanced multi-agent orchestration framework capable of real-time conversational reasoning and tool execution over massive enterprise datasets while fostering an AI-native engineering culture.
This position reports directly to our Senior Director of Engineering and has the opportunity to be based in our San Francisco office.
What you’ll do
- Drive Technical Strategy: Own the architectural roadmap and delivery of Dialpad’s Agentic infrastructure, core orchestration layers, memory architectures, and evaluation/observability systems.
- Build & Scale: Design and deploy scalable, multi-modal AI agents capable of autonomous support, real-time voice reasoning, and secure API tool execution across complex enterprise workflows.
- Mentor & Influence: Act as a technical anchor for the organization, raising the engineering bar, mentoring senior peers, and defining technical standards for an AI-native SDLC.
- Partner Cross-Functionally: Collaborate with leadership across Product, Engineering, and Applied Research to align technical execution with Dialpad’s long-term business strategy.
- Push the Frontier: Research and implement emerging agent frameworks, LLM inference optimization, advanced retrieval systems, and cutting-edge safety/policy guardrails to keep Dialpad at the absolute forefront of the "era of the agent."
Skills you’ll bring
- Experience: 10+ years of relevant software engineering experience, with a proven track record of technical leadership (as a Staff, Senior Staff, or Principal Engineer) shipping complex, large-scale systems.
- Systems Background: Strong foundations in scaling distributed systems and production-grade infrastructure before evolving into applied AI, LLM platforms, and agentic architectures.
- Leadership: Ability to lead and grow a small group of engineers.
- LLM Platforms: Inference optimization and fine-tuning strategies.
- Data & Retrieval: Advanced retrieval systems and memory architectures.
- Agent Frameworks: Hands-on experience with frameworks like LangChain/LangGraph, CrewAI, or AWS/Google Agent ecosystems.
- AI Ops: Evaluation, observability, and safety frameworks for production AI systems.
- Real-Time Infrastructure: Streaming infrastructure and voice/conversational AI.
- Tool Integration: Tool use, API execution frameworks, and human-in-the-loop validation systems.
- Operational Excellence: Experience setting clear technical goals, identifying architectural risks, and systematically clearing tech-debt gaps.
- The 0→1 Archetype: Ability to thrive in ambiguity, build cutting-edge AI products from the ground up, and scale them into robust, self-sustaining enterprise systems.
