AI-Native Pod Leader

Role summary

Location: Herzliya, Tel Aviv District, Israel
Hybrid / Full-time

The AI-Native Team Leader is a new kind of engineering leader at Payoneer — equal parts technical authority, people developer, and AI-first builder. You’ll lead a small AI Pod that owns its problems end-to-end: from understanding the business need, through architecture and implementation, to production monitoring. There are no hand-offs, no tickets, no specs thrown over a wall. Your team builds with AI as a primary tool, ships fast, and sets the methodology for how R&D works next.

What you’ll do

• Lead and develop a small 3-4 engineers AI Pod, with a clear focus on their technical growth, autonomy, and delivery outcomes
• Provide technical authority hands-on — code, architect, review, and ship alongside your team
• Own the full delivery loop: understand the business problem, define the solution, architect it, ship it, monitor it — no one hands you a spec
• Collaborate directly with Product, Design/UX, DevOps, and business stakeholders to define decision logic, risk thresholds, and success metrics — building capabilities, not features
• Drive AI-native development — Claude Code, Cursor, and whatever tools give your team the most leverage; build in a day what used to take a team a week
• Build the harness before you ship — evaluation frameworks, monitoring, and observability go in on day one, not after the first incident
• Be accountable for overall design, architecture, code quality, and production environment across your team
• Set a higher technical bar — run design reviews, champion engineering best practices, and push for continuous improvement in code quality and team craft
• Define the methodology — this is early-stage work within Payoneer; you won’t inherit a playbook, you’ll write it

Who you are

• 5+ years as a backend or full-stack engineer with real production experience — you’ve watched systems run in the real world and fixed what breaks
• 2+ years leading engineering teams — you’ve grown people, driven delivery, and you know the difference between managing and developing engineers
• Experience shipping production AI systems — agentic architectures, orchestration, multi-step workflows, fallback and error logic; you think in workflows, not endpoints
• Real fluency with AI coding tools — not “familiar with,” genuinely fast with them
• Strong eval instincts — you define metrics, build test sets, and don’t ship until you can measure; evaluation frameworks are part of the architecture, not an afterthought
• Full-stack comfort — you move from the prompt to the RAG pipeline to the orchestrator to the backend and back; you go where the right solution is
• Experienced and passionate about managing and growing people — strong opinions, held loosely; you make space for different perspectives and help the people around you grow
• Honest communicator — you flag problems early, give direct feedback, and say what you actually think; experienced with engineering best practices (code reviews, testing coverage, agile methodologies)
• Grounded confidence — you trust your abilities, move forward with incomplete information, make a call, and own it; when you’re wrong, you update and move on

Not a must but a great advantage:

• Fintech or regulated-environment experience — what matters is understanding why “probably fine” isn’t acceptable when money is moving
• Experience with messaging systems such as Kafka, RabbitMQ, or SQS
• Experience building SaaS platforms in a cloud-based, microservices environment
• Experience with frontend technologies such as React or Angular

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