Software Engineer (Agentic Systems)

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 Bay Area 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.