Senior Team Leader, AI Engineering

About the role

This role owns the engineering craft, execution, and growth of the engineers on Rowan. It is a role for an engineering leader who is excited by and experienced in AI as an execution layer, not just a conversation layer.

The Rowan team is building the next generation of a personal finance assistant that can notice what matters, reach out at the right moment, and help finish the job. Rowan’s already serving users with capabilities such as organizing someone’s finances, helping cancel or negotiate a bill, or surfacing the next action that gives a customer time, money, or peace of mind back.

Responsibilities

• Lead, develop, and grow the Rowan engineering team, setting a high bar for technical judgment, product quality, customer trust, and steady execution.
• Partner with product leadership to translate Rowan’s strategy (be the agent that finishes the job) into engineering plans and shipped capabilities that create measurable customer value.
• Stay close to the full Rowan experience: conversational interfaces, orchestration, memory, app surfaces, detection systems, operational rails, human escalation paths, and the safety models required to act on someone’s finances.
• Help the team build agentic capabilities that do consistent, correct and reliable work end-to-end.
• Drive projects to a conclusion by making decisions, clearing blockers, aligning stakeholders, and helping the team consistently ship high-quality work customers can feel.
• Create the systems and habits that make AI work dependable: eval frameworks, observability, approvals, fallbacks, recovery paths, human-in-the-loop workflows, and clear ownership.
• Partner closely with Product, Design, Data, Operations, Legal, Support, and Engineering leadership to help choose the highest-leverage bets, make crisp tradeoffs, and bring an engineering point of view to what Rowan should and should not take on.
• Onboard a team environment where senior engineers can stretch across product, AI, systems, operations, and trust while doing some of the best work of their careers.
• Hold regular 1:1s, deliver performance reviews, build individualized growth plans, and tailor your coaching to each person’s strengths, ambitions, and working style.
• Help raise the product bar beyond chat toward proactive, rewarding, trustworthy experiences that make Rowan feel like one assistant everywhere.
• Stay curious about the frontier of AI engineering and help the team adopt new workflows, tools, and patterns where they make the work better.

Requirements

Strong candidates may have:

• Experience building or leading teams that combine probabilistic AI systems with deterministic execution paths, operational workflows, or human-in-the-loop review.
• Experience with TypeScript, Node, React, React Native, GraphQL, Postgres, LLM applications, agentic systems, workflow orchestration, or consumer fintech products.
• A track record of creating team infrastructure from the ground up: hiring practices, onboarding, technical playbooks, coaching systems, review rituals, or quality frameworks.
• Experience improving reliability, observability, alerting, incident response, operational quality, or other systems that make software safer and more dependable over time.
• Experience partnering deeply with Product, Design, Data, Operations, Legal, Support, or other cross-functional teams in a sensitive customer domain.
• Firsthand experience building products where customer trust, permissions, explainability, and recovery paths are central to the experience.

Qualifications

• Experience shipping production agentic software and a strong track record leading engineering teams, including senior ICs working in complex or ambiguous domains.
• An engineer who leads engineers. You can set direction and grow people while staying close enough to the work to pair on hard problems, review system design, write code at the right moments, and model strong engineering judgment.
• A strong product judgment. You can reason from customer need to product experience to technical architecture, and you know the difference between a clever demo and a durable customer-facing system.
• An excitement for AI as an execution layer. You understand that LLMs are powerful, but that real customer value comes from orchestration, tools, workflows, evaluation, approvals, observability, and recovery.
• Hands-on or deeply practical familiarity with LLM applications, agentic systems, workflow orchestration, evals, and the tradeoffs involved in moving from prototype to production.
• A high bar for quality and trust. You know that an assistant acting on personal finances has to be accurate enough to earn the right to act, and you are comfortable slowing down when the alternative would spend customer trust.
• A comfort with ambiguity. You can take a broad strategic mandate, identify the crux, sequence the work, make tradeoffs, and keep a team moving without waiting for perfect clarity.
• A tenacious learner. You stay engaged with what is changing in AI, team workflows, product expectations, and operational systems without chasing every shiny object.
• A habit of continuous improvement. You notice where a workflow is quietly painful, where an eval is too vague, where a recovery path is missing, or where the team has accepted too much uncertainty as normal.
• A visible technical depth and range across some combination of backend services, product surfaces, data systems, AI workflows, operational tooling, or consumer fintech products.
• A comfort with urgency, but not recklessness. You can help a team move quickly in a short strategic window while keeping both hands on the wheel.