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Ooak Data seeks an Engineering Lead to run one of three squads: ingestion, anonymization and processing, or QA and delivery. The role requires a decade of software experience and several years of people management, with strong backend skills in Python or Rust, SQL, distributed pipelines and cloud.
The lead will code and review daily, while also handling hiring, one-on-ones and team habits, with a focus on quality and data security given the sensitive data involved.
This is a hands-on engineering lead job running one of three product squads: ingestion, anonymization and processing, or QA and delivery. The role asks for a decade of software experience, several years managing engineers (including senior ones), and solid back-end skills in Python or Rust, SQL, distributed pipelines and cloud. A strong candidate codes and reviews daily while also handling hiring, one-to-ones and team habits, and cares a lot about quality and security because the data is sensitive.
The company's whole product exists because AI agents need realistic company data to learn how to do real work, so the role sits close to how frontier models get trained. Day to day, engineers work with several coding agent sessions at once, so the lead is expected to set practices that make a team faster without letting quality slip. What stays human is judgment on data privacy, review standards, architecture choices, and growing and guiding people.
Someone in this role would gain close exposure to how reinforcement learning environments are built and checked against leading models, plus daily practice running a team that builds with coding agents. For those who want to prepare, trying agent-based coding on a real data pipeline project and reading up on how AI agents are evaluated would be a useful start. Any work on anonymizing data or handling sensitive records adds directly to the fit.
This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.
Ooak Data builds reinforcement learning environments from real company data so AI agents can be trained and evaluated on expert-level tasks, per the company.