Engineering Manager (AI) - 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