AI Infra Engineer

We’re partnering with a category-defining AI company building one of the fastest-growing consumer products in the world. At the core of their success is a powerful data flywheel - every interaction improves the system, and every improvement drives more usage. They’re now hiring engineers to build and scale the infrastructure behind that loop. This is a role for someone who wants to sit at the intersection of product, data, and machine learning , working on systems that directly impact model quality and user experience. The Role You’ll work on the pipelines and systems that transform raw user interactions into actionable intelligence - fueling continuous improvement across the product. This is high-impact, product-facing engineering with a strong data component. You’ll: Build systems that capture, process, and structure large-scale user interaction data Design pipelines that feed data back into model training and evaluation loops Improve data quality, reliability, and observability across the platform Work closely with ML and product teams to translate insights into better outputs Help scale infrastructure that powers a rapidly growing global product What We’re Looking For This role suits engineers who enjoy working across backend systems and data-heavy environments. You likely have: Strong backend engineering experience (Python, Go, or similar) Experience working with data pipelines, ETL systems, or large-scale datasets Familiarity with distributed systems and high-throughput architectures An interest in machine learning systems or data-driven products A product mindset—you think about how systems translate into better user outcomes Comfort operating in fast-moving, ambiguous environments Why This Role? You’ll directly influence how an AI system improves itself over time You’ll work on the feedback loop that powers product-market fit and retention You’ll be part of a company scaling extremely quickly in the AI space You’ll operate in a high-ownership environment where engineers shape product direction Ideal Profile This is a strong fit for someone who: Enjoys building systems that sit between product and ML Thinks in terms of feedback loops, not just features Wants exposure to both infrastructure and model improvement Is motivated by impact, scale, and speed #J-18808-Ljbffr Harrison Clarke