Engineering Manager (AI) - Supernal

Job description

ABOUT SUPERNAL At Supernal, we help SMBs hire their first AI employee. Our AI teammates are built with intelligent, agentic workflows and deployed on our proprietary platform. We don't build tools — we deliver working, value-generating AI Employees. Our AI Platform Engineers, known internally as Masons, are the builders behind these systems. As we scale delivery, we need a Mason Manager to lead multiple pods of Masons and ensure we ship reliable, production-grade AI Employees — predictably and at high quality. THE ROLE As a Mason Manager (Engineering Manager), you will lead multiple pods of Junior + Senior Masons responsible for building and shipping production automation and agentic systems for customers. This is a highly technical people leadership role. You will be accountable for what your pods ship: architecture decisions, quality bars, reliability, documentation, and delivery outcomes. You’ll also invest heavily in hiring, coaching, and performance management — building a team that can deliver at scale with consistent craft. You are not a “process-only” manager. You will stay close to the work: reviewing designs, unblocking complex integrations, setting engineering standards, and acting as the escalation point for production issues and delivery risk. RESPONSIBILITIES - Lead multiple Mason pods and own delivery outcomes: scope, milestones, quality, and on-time execution - Translate ambiguous customer/internal requests into clear plans, acceptance criteria, and execution strategy - Set and enforce production-quality standards for Mason builds (testing, monitoring, runbooks, documentation, rollout plans) - Serve as technical escalation for difficult problems: auth/permissions, integrations, data modeling, reliability, and failure recovery - Establish and evolve team processes: scoping discipline, QA gates, review checklists, incident/postmortem loops, and continuous improvement - Drive prioritization and capacity planning across pods; identify the critical path and remove blockers fast - Partner with Delivery Leads and stakeholders to manage tradeoffs, timelines, and expectations (including client-facing escalations when needed) - Hire and build the team: define roles, run interview loops, calibrate, close candidates, and improve onboarding - Manage performance: set expectations, deliver feedback, coach growth, and handle underperformance clearly and fairly - Develop leaders within the Mason org: mentoring, delegation, and building strong ownership at every level YOU MIGHT BE A FIT IF YOU... - Have 5+ years of experience building production systems as a software/automation engineer, plus 2+ years of engineering management or tech-leadership experience (people management strongly preferred) - Have managed multiple concurrent workstreams (pods/squads) with shared standards and predictable delivery - Are deeply comfortable with integrations: APIs, webhooks, auth (OAuth/API keys), and data stores (Postgres/Supabase) - Can reason about reliability in automation/agentic systems: idempotency, retries/backoff, rate limits, auditing, and safe failure modes - Have a strong quality mindset: unit/integration/E2E testing, regression prevention, monitoring/observability, and runbook culture - Have experience with applied AI delivery patterns: prompt iteration, eval harnesses, human-in-the-loop QA, and LLM observability - Enjoy people management and have real examples of coaching, feedback, and performance management - Have run hiring loops end-to-end: defining roles, interviewing, calibration, and closing candidates - Communicate clearly and fluently in English — written and verbal — and can align technical and non-technical stakeholders - Thrive in fast-paced, ambiguous environments and take ownership without being asked WHAT SUCCESS LOOKS LIKE - Multiple Mason pods ship production AI Employees predictably, with clear milestones and minimal thrash - Builds are reliable in the wild: fewer incidents, fast recovery, strong observability, and durable runbooks/SOPs - Engineering standards are consistently applied across pods (testing, documentation, QA gates, and design clarity) - Stakeholders have high trust: timelines and tradeoffs are communicated early and crisply - The Mason org scales through strong hiring and onboarding; new Masons ramp quickly and ship meaningful work - Team performance improves over time through coaching, clear expectations, and a high-accountability culture