Senior Manager of AI Engineering, ServiceTitan

• We do not expect one person to have built every part of an agent platform before. We do expect strong engineering judgment, production scars, hands-on curiosity, and the ability to learn fast while making high-quality technical decisions
• 8+ years of software engineering experience, including 4+ years leading engineering teams or major technical initiatives in a product or platform organization
• Strong technical background as a builder. You may not write production code every day, but you can read code, review implementation plans, reason through distributed systems, and debug real behavior
• Recent hands-on technical leadership: you have personally reviewed design docs, read implementation details, inspected production traces/logs, or debugged system behavior in the last 6–12 months
• Experience shipping AI, ML, data, platform, infrastructure, workflow, automation, or developer-platform systems in production
• Practical understanding of modern LLM application architecture: model gateways, prompt/context assembly, retrieval, tool calling, structured outputs, memory, agent workflows, and human approval patterns
• Strong instincts for production agent safety: typed tools, scoped permissions, business invariants, precondition checks, approval thresholds, reversible actions, idempotency, audit trails, and rollback
• Production-minded approach to evaluation: scenario design, behavioral evals, regression suites, trace review, simulation, offline/online metrics, and monitoring for non-deterministic systems
• Strong engineering judgment across APIs, distributed systems, event-driven systems, data platforms, observability, reliability, security, and multi-tenant SaaS constraints
• Strong data and context instincts: SQL, unstructured data, vector search, metadata, provenance, source authority, freshness, and privacy boundaries
• Ability to turn ambiguous strategy into sequenced roadmaps, measurable outcomes, and clear ownership
• Clear communication with engineers, product leaders, architects, security partners, and executives
• Low-ego coaching style. You raise the technical bar while helping the team move faster
• If you have the skills but are hesitant to apply because of your background, apply anyway

Desirable

• We are looking for a Senior Engineering Manager to lead a small, hands-on AI platform team building the core Agent OS. This is a builder-manager role
• The right person can lead engineers, shape architecture, make high-quality technical decisions, and stay close enough to the work to unblock design, implementation, debugging, evaluation, and production delivery
• This is not a pure people-management, AI strategy, or research leadership role. We need someone who can earn credibility with senior engineers by improving the technical work, not just coordinating it
• You will lead a compact AI platform engineering team responsible for foundational Agent OS capabilities, including:
• Agent runtime and workflow execution: role-specific agents, planning, tool use, delegation, pause/resume, long-running workflows, durable checkpoints, and failure recovery
• Context and memory systems: retrieval, tenant-aware memory, transcripts, artifacts, tool results, provenance, freshness, and replayable evidence
• Capability platform: reusable domain capabilities that combine prompts, tools, context requirements, policies, evals, rollout controls, ownership, and rollback expectations
• Action and trust layer: typed action contracts, scoped permissions, business precondition checks, approval flows, reversibility, idempotency, audit trails, and human-in-the-loop controls
• Evaluation and observability harness: offline and online evals, scenario libraries, simulation, trajectory review, regression detection, quality metrics, cost/latency telemetry, and autonomy promotion gates
• ServiceTitan integration: secure access to systems of record, governed data sources, domain context, Atlas integration, and role-specific agent experiences for owners, CSRs, dispatchers, technicians, managers, accountants, and back-office teams
• Lead the team through architecture, implementation, production launch, and fast iteration
• Stay hands-on: review designs and code, inspect traces, debug production behavior, evaluate prototypes, and help engineers make pragmatic tradeoffs
• Translate Agent OS strategy into concrete platform slices that ship quickly without creating one-off agent implementations
• Define platform contracts for role shells, capabilities, tools, actions, approvals, context, memory, evidence, and evaluation
• Build the distinction between what an agent can do and what it is authorized to do in a given tenant, role, workflow state, and risk context
• Partner with Product, Design, Architecture, Security, Data Platform, Atlas, and domain engineering teams to create useful, safe, and measurable agent capabilities
• Drive evaluation as part of everyday engineering: scenario design, regression suites, trace review, simulation, production monitoring, quality gates, and rollout criteria
• Help the team make model and inference tradeoffs across latency, cost, quality, structured outputs, caching, fallback behavior, and provider choices
• Ensure live ServiceTitan systems of record remain authoritative while memory, retrieval, transcripts, and agent-generated artifacts are governed as contextual evidence
• Work through real agent failures with the team: wrong tool calls, stale context, missing permissions, unsafe actions, poor retrieval, bad recommendations, latency spikes, and cost regressions
• Hire, coach, and retain strong engineers who can build fast, reason deeply, and operate responsibly in a fast-moving AI environment