Machine Learning Engineer - Quality Intelligence

AfterQuery is an applied research lab curating data solutions for foundation model development.

We serve every frontier AI lab with the mission of delivering the best data to power the best models. In doing so, we can make expertise that once took a lifetime to build available to anyone who needs it. Our customers are the ones building the foundation models themselves and our work sits directly in the loop of how those systems improve.

This is a rare opportunity to join a company at a defining moment in AI. Since raising our $30M Series A at a $300M valuation, AfterQuery has grown well over a $100M revenue run rate.

We're based in San Francisco and backed by leading investors including Altos Ventures, BoxGroup, and Y Combinator and angels from Google DeepMind, OpenAI, Anthropic, Meta Superintelligence Labs, and Microsoft AI.

Massive Opportunity:
We were one of the fastest-growing YC companies in our batch, and we believe we can become one of the fastest-growing YC companies of all time.

Founding Impact:
You will own and architect core infrastructure systems that power our platform from the ground up.

Equity & Growth:
Competitive salary and meaningful equity. As we scale, you’ll have the opportunity to shape the engineering organization and lead major technical initiatives.

Strong Team:
Our founding team has experience from Citadel Securities, Meta, Google, Silver Lake, and Morgan Stanley — work alongside world-class engineers and researchers.

AfterQuery builds the data and evaluation systems that power frontier AI models. Every leading AI lab uses our datasets and reinforcement learning environments to encode and scale real-world expertise.

We’re hiring a Founding Machine Learning Engineer, Quality Intelligence to build the ML systems behind how we measure, improve, and scale data quality. You’ll work on production systems at the intersection of machine learning, human expertise, and frontier model evaluation.

This role is for someone who wants to build practical ML systems that directly improve the quality, reliability, and scalability of expert human data.

Build ML and data systems that help measure quality across complex human data workflows

Develop systems for expert matching, quality prediction, and anomaly detection

Build evaluation infrastructure for tasks, reviewers, projects, and data deliveries

Turn messy real-world signals into models, metrics, and product improvements

Partner with engineers, domain experts, and operators to improve how high-quality data is created and reviewed

Own high-impact systems from early design through production deployment

3-6 YOE with relevant experiences

Strong software engineering background with experience shipping production systems

Experience with applied ML, ranking, recommendations, search quality, marketplace systems, trust/safety, fraud, or data quality systems

Strong data intuition and ability to work with messy, ambiguous real-world signals

Comfort working across backend systems, data pipelines, ML models, and internal tools

Ability to move quickly in a high-ownership, fast-changing environment

Deep care for quality, precision, and customer impact

• You want to do pure research without owning production systems

• You only want to train models and not build product infrastructure

• You need clean datasets and perfectly scoped problems

• You do not want to work closely with users, operators, and domain experts