Machine Learning Engineer - Quality Intelligence
AfterQuery · San Francisco, California, United States · $200K–$300K/yr
Software Development · 51-200 employees
About the role
Build and scale ML systems to measure and improve the quality of complex human data workflows. Develop evaluation infrastructure and turn real-world signals into metrics and product improvements for frontier AI models.
What they look for
Requirements
Requires 3-6 years of experience in shipping production systems with a strong background in applied ML or data quality. Candidates must be comfortable working across backend systems and data pipelines in a fast-paced, high-ownership environment.
Benefits
Full description
About AfterQuery
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.
Machine Learning Engineer, Quality Intelligence
Overview
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.
Responsibilities
- 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
Required Qualifications
- 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
Not a Fit If
- 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
