infinity-constellation - Engineering Manager (AI) - Supernal

• 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