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MURAL is seeking an Engineering Manager to lead the Compose team, an ambitious, fast-moving group building AI-native products at the forefront of the company's product strategy and growth. This is a hands-on leadership role where you'll take an early-access AI product to production maturity while scaling a high-performing remote-first team.
You will lead, hire, and develop a team of full-stack engineers through regular 1:1s, clear expectations, coaching, and performance management. You'll own delivery end-to-end with a fast, experimental loop: ship early to real users, measure results, and iterate quickly without sacrificing reliability. You'll partner closely with Product and Design as a co-owner of the Compose roadmap, using customer feedback and usage data to prioritize what to build, cut, or double down on.
As a hands-on manager, you'll review pull requests, set technical standards, and stay close to code and architecture decisions. You'll raise engineering craft across testing, observability, and AI-assisted development while modeling MURAL's remote-first, work-in-public culture at a sustainable pace. You'll keep the team's pace high while maintaining psychological safety and sustainable working conditions.
The tech stack includes Azure OpenAI, React, Node, and MongoDB. This role requires someone who can balance strategic thinking with technical credibility, lead with empathy, and thrive in a fast-moving, experimental environment.
REQUIREMENTS:
- 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
- Solid technical foundation sufficient to lead architecture discussions and review code with credibility
- Bias for experimentation: make sound calls quickly, learn from data, and change course based on evidence
- People-first approach: lead with empathy, listen first, and build trust and psychological safety
- 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
- Experience building AI agents, retrieval systems, knowledge graphs, or familiarity with Model Context Protocol (MCP)
