Engineering Manager (AI) - Supernal

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

Requirements

• 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)
• Managed multiple concurrent workstreams (pods/squads) with shared standards and predictable delivery
• Deeply comfortable with integrations: APIs, webhooks, auth (OAuth/API keys), and data stores (Postgres/Supabase)
• Reason about reliability in automation/agentic systems: idempotency, retries/backoff, rate limits, auditing, and safe failure modes
• Strong quality mindset: unit/integration/E2E testing, regression prevention, monitoring/observability, and runbook culture
• 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
• 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

Qualifications

• Experience with applied AI delivery patterns: prompt iteration, eval harnesses, human-in-the-loop QA, and LLM observability
• Experience with fast-paced, ambiguous environments and taking ownership without being asked