Software Engineer (Agentic Systems

Job Description

Job Description

Dialpad is the AI platform for customer experience, built to resolve customer problems in real time across voice and digital. Our AI agents learn from your best human agents and improve with every interaction, helping organizations understand their customers, deliver better experiences, increase operational efficiencies, and build a lasting competitive advantage.

Unlike legacy systems built to route and answer, or standalone agentic bot vendors built to deflect, Dialpad was built to resolve. Our AI agents and human agents operate on a single platform with shared context, allowing Agentic AI to resolve issues, advance deals, and eliminate busywork through automation while seamlessly handing conversations to humans when needed, with full context preserved.

Market-leading brands, including Randstad, Motorola Solutions, Netflix, the San Diego Padres, the Colorado Rockies Baseball Club, and Cal Athletics, trust Dialpad. Dialpad is backed by Andreessen Horowitz, GV, ICONIQ Capital, and T-Mobile.

At Dialpad, AI isn't just a feature; it's how our teams do their best work every day. We put powerful AI tools in every employee's hands so they can move faster, think bigger, and achieve more.

We believe every conversation matters. And we've built the platform that turns those conversations into insight and action, for our customers and ourselves.

We look for people who are intensely curious and hold themselves to a high bar. Our ambition is significant, and achieving it requires a team that operates at the highest level. We seek individuals who embody our core traits:
Scrappy, Curious, Optimistic, Persistent, and Empathetic
.

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.

• 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.

• 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 Lang Chain/Lang Graph, 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 Excellen