AfterQuery
Machine Learning Engineer - Quality Intelligence
About this role
AfterQuery seeks a founding Machine Learning Engineer to build systems that measure and improve data quality for frontier AI model development. You'll design ML infrastructure for expert matching, quality prediction, and anomaly detection while partnering across engineering, operations, and domain expertise to scale high-quality human data workflows.
What you'll do
- Build ML and data systems to measure quality across human data workflows
- Develop expert matching, quality prediction, and anomaly detection systems
- Create evaluation infrastructure for tasks, reviewers, projects, and data deliveries
- Convert real-world signals into actionable models, metrics, and product improvements
- Partner with engineers, domain experts, and operators on data creation and review processes
- Own high-impact systems from design through production deployment
What they're looking for
- Applied machine learning (ranking, recommendations, search quality)
- Production systems engineering and shipping
- Data quality and anomaly detection
- Backend systems and data pipelines
- Marketplace or trust/safety system experience
- Working with messy, ambiguous real-world data
- Internal tools and infrastructure development
- Cross-functional collaboration and communication
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AfterQuery
AfterQuery is an applied AI research lab that builds data infrastructure and evaluation frameworks powering foundation model development for frontier AI labs. The company is hiring fullstack software engineers, infrastructure/security specialists, and interns to design scalable data pipelines, develop datasets and reward signals, and create systems that directly influence how advanced AI models are trained.
View all jobs at AfterQueryLikely interview questions
- Walk us through a production ML system you built from design to deployment—what were the key challenges with data quality or measurement?
- Describe your experience working with messy, real-world data signals. How did you convert them into reliable models or metrics?