Engineering Manager (Agentic Systems)

Dialpad•San Francisco, CA

4d•$249,500 - $273,500•Onsite

About The Position

As a Tech Lead Manager on our Agentic AI initiatives, you will play a hybrid leadership role across technical direction, execution, and people management. You will help shape and deliver Dialpad’s next-generation AI platform, building production-grade systems where AI agents reason, act, coordinate, and safely execute workflows. Working closely with Product, Applied Research, Design, and Engineering leadership, you will lead a small team of engineers while remaining deeply involved in architecture, technical decision-making, and delivery. This role is ideal for a leader who can alternate between hands-on technical leadership and engineering management, helping the team move from concept to reliable production systems. You will contribute to the development of multi-agent orchestration frameworks capable of real-time conversational reasoning and tool execution over large enterprise datasets, while fostering a strong 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.

Requirements

10+ years of relevant software engineering experience, with a proven track record of technical and engineering leadership (as a Tech lead or a first line manager) shipping complex, large-scale systems.
Experience in AI, ML platforms, LLM systems, or agentic architectures is strongly preferred.
Strong foundations in scaling distributed systems and production-grade infrastructure before evolving into applied AI, LLM platforms, and agentic architectures.
Ability to lead and grow a small group of engineers.
You have shipped production systems where AI agents reason, act, coordinate, and safely execute workflows. You bring deep expertise in:
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.
Technical leadership: You can set direction for a team, identify key architectural risks, and guide execution without losing sight of detail.
People leadership: You can coach engineers effectively, give clear feedback, manage performance, and support career growth.
Operational excellence: You know how to create clarity, define measurable goals, and systematically close quality, reliability, and technical 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.

Responsibilities

Lead Technical Direction: Own the technical direction and execution for a meaningful area of Dialpad’s agentic AI platform, including memory architectures, retrieval systems, and evaluation or observability capabilities.
Build and Scale: Stay close to the code and design work, contributing hands-on where needed to unblock the team, accelerate critical efforts, and maintain a high technical bar. Help design and deploy scalable AI systems that support autonomous assistance, real-time voice reasoning, and secure API or tool execution across enterprise workflows.
Manage and Grow a Team: Lead and grow a small team of engineers through coaching, feedback, career development, and performance management. Create clarity around priorities, ownership, and execution, and help the team operate effectively through ambiguity. Partner on hiring and onboarding to build a strong, high-performing team.
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 pragmatically: Evaluate emerging agent frameworks, inference optimization techniques, retrieval approaches, and safety guardrails, and apply them where they create meaningful product or platform advantage.
Raise the Engineering Bar: Establish strong engineering practices across design review, operational readiness, testing, quality, and AI safety. Mentor engineers and tech leads, reinforce sound technical judgment, and help define standards for an AI-native software development lifecycle.