About The Position

Apple’s Wallet, Payments, and Commerce organization builds the financial services that hundreds of millions of people rely on daily — Apple Pay, Apple Card, Apple Cash, Access, Transit, and more. These are regulated, security-critical, privacy-first systems operating at global scale, where availability is table stakes. We’re forming a new enablement team with a specific thesis: GenAI-first development is ready to move from experiment to operating model. We proved this by building a large-scale financial micro-services application in an automated run, and now we need engineers who can make that approach repeatable, rigorous, and accessible across our engineering organization. This role is about engineering judgment applied at a new layer of the GenAI stack — not prompt crafting. It demands more rigor, not less. You’ll engineer the workflows, judgment frameworks, and validation tools that let LLMs generate production-quality code — then make this approach accessible to engineering teams across the organization. You’ll build the automated validations that push the boundary of GenAI-first development, concentrate expert judgment, and feed what vertical teams learn back into the platform. Expert judgment is core to this role — knowing when to automate, when to include the expert in the loop, when an approach will scale to the whole org.

Requirements

  • Demonstrated ability to adopt new development paradigms and apply them to real problems with concrete examples (e.g., GenAI-driven development, infrastructure-as-code, test automation frameworks, CI/CD transformation, or comparable shifts in how a team builds software).
  • Deep domain expertise.
  • Clear communication across engineers, engineering leaders, and quality engineering.
  • 8+ years of prior software engineering experience.
  • At least 1 year dedicated to the following areas: Defined and evolved GenAI development automation, templates, and workflow patterns.
  • Built, acquired, and spec the validation tools that expand what GenAI-first development can reliably automate.
  • Designed and ran experiments that push the limits of the approach and define the metrics that prove what’s working.
  • Built the compliance, security, and quality validations into the automation.

Nice To Haves

  • Fluency with GenAI-first development tools such as AI coding assistants, LLM-driven automation frameworks, or similar platforms.
  • Experience in financial services, payments, or regulated technology domains.
  • Automated validation, quality engineering, or test automation at scale.
  • Establishing a new team or function: defining practices, engagement models, and priorities from scratch.

Responsibilities

  • Engineer the workflows, judgment frameworks, and validation tools that let LLMs generate production-quality code.
  • Make the GenAI-first development approach accessible to engineering teams across the organization.
  • Build automated validations that push the boundary of GenAI-first development.
  • Concentrate expert judgment within the GenAI platform.
  • Feed learnings from vertical teams back into the platform.
  • Determine when to automate, when to include an expert in the loop, and when an approach will scale to the whole organization.
© 2026 Teal Labs, Inc
Privacy PolicyTerms of Service