Mural Engineering Manager at Mural leading the Compose team to develop AI-native products from early access to reliable daily use. Responsible for technical leadership, team growth, and partnership with Product and Design to drive the roadmap.
Responsibilities
Lead, hire, and grow a team of full-stack engineers through regular 1:1s, clear expectations, coaching, and performance management.
Own delivery end to end with a fast, experimental loop: ship early to real users, measure what works, and iterate quickly without losing reliability.
Partner with Product and Design as co-owners of the Compose roadmap, using customer feedback and usage data to decide what to build, cut, or double down on.
Raise engineering craft across testing, observability, and AI-assisted development, and model Mural's remote-first, work-in-public culture at a sustainable pace.
WHAT YOU'LL BRING
2+ years managing software engineers directly, with responsibility for hiring, coaching, and performance management.
5+ years of prior experience as a software engineer building and operating production applications, with hands-on technical work in the last 3–4 years.
A solid technical foundation, enough to lead architecture discussions and review code with credibility.
A bias for experimentation: you make sound calls quickly, learn from data, and change course when the evidence says so.
A people-first approach: you lead with empathy, listen first, and build the trust and psychological safety that help each person do their best work.
Excellent communication skills, including explaining technical tradeoffs clearly to product, business, and customer stakeholders.
Experience leading a team that shipped LLM-powered features to production, including evaluation, quality monitoring, and cost management.
Qualification
Experience building AI agentsExcellent communication skills
Required
2+ years managing software engineers directly, with responsibility for hiring, coaching, and performance management
5+ years of prior experience as a software engineer building and operating production applications
Hands-on technical work in the last 3–4 years
Solid technical foundation to lead architecture discussions and review code
Experience leading a team that shipped LLM-powered features to production, including evaluation, quality monitoring, and cost management
Experience building AI agents, retrieval systems, knowledge graphs, or familiarity with Model Context Protocol (MCP)
