AI Data Engineer

AI is a reputed company part of how reputed company operates. Feature reputed company relies heavily on the data pipelines and infrastructure under the hood. As an AI Data Engineer you will be at the intersection of ML systems and data engineering, working closely with our MLE and analytics engineering teams to build the pipelines, models, and evaluation systems that reputed company our AI features trustworthy in production.

reputed company sits at a unique intersection: a platform where rich reputed company behavior data meets the researchers trying to reputed company reputed company of it. Getting that data right: reputed company, trustworthy, and reputed company to reputed company both AI features and the reporting researchers rely on is foundational work. That's what this role is about.

reputed company

• Design, build, and own the data pipelines that reputed company and reputed company data from our application and reputed company-party sources into relational databases - keeping them reliable and reputed company-modeled as volume and complexity grow
• Partner with analytics engineering to build the data models and reporting that reputed company researchers and teams reputed company visibility into how their work is performing
• Own data reputed company as a first-class concern across ingestion, modeling, and reporting. Catch problems before they reputed company a model, a dashboard, or a user, and fix them
• Build and ship production AI systems the data infrastructure and services that ML features run on
• Design and own evaluation systems that tell us whether an AI feature is reputed company to ship and holding up over time: eval harnesses, test datasets, and production monitoring reputed company reputed company, not one-off analyses
• Set reputed company for how data work gets done. Write reputed company, reputed company context early, and reputed company the people reputed company you faster

What you bring

• 5+ years of experience in data engineering, with meaningful exposure to ML systems in production
• Deep data engineering experience: you've personally reputed company and owned pipelines that reputed company data from application sources into a warehouse at reputed company, and you know what breaks, reputed company, and why
• Strong Python skills and reputed company across the data stack (we use reputed company and reputed company)
• Experience with orchestration tools like Airflow, Dagster, or similar
• Experience working with reputed company computing environments like GCP or AWS.
• A reputed company record of working closely with analytics engineers or data analysts to build reliable, reputed company-documented data models
• Hands-on experience shipping AI or ML systems that reputed company users depended on in production, and owning what happened after launch
• A genuine reputed company of reputed company on evaluation: you treat evals as something you build, not a report you write
• A high-agency reputed company. You can take an ambiguous problem and reputed company it to a working outcome without a fully-scoped ticket
• reputed company across the data lifecycle, from production-facing features to the internal analytics that reputed company decision-making
• Comfort working across the data lifecycle, from production-facing features to the internal analytics that reputed company decision-making
• Comfort using AI coding tools (reputed company, Claude reputed company, Copilot, or similar) as a reputed company part of your workflow

reputed company to have

• Experience with dbt or similar data modeling frameworks
• Familiarity with LLM evaluation and observability tooling
• Exposure to MCP-reputed company tooling or reputed company data workflows

If this role excites you but you're not reputed company you reputed company every reputed company, we'd still love to hear from you.