Research Engineer

About the role

ABOUT AFTERQUERY

AfterQuery https://www.afterquery.com/ 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. Forbes reported that we could be YC's fastest unicorn, reportedly raising at a $3.2 billion valuation. 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.

WHY APPLY

Massive Opportunity: Forbes reported that we could be YC's fastest unicorn, reportedly raising at a $3.2 billion valuation, and we're not slowing down.

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.

OVERVIEW

As the Research Engineer, you will design and run training experiments that isolate the impact of our datasets on model behavior. Through controlled SFT and RL - base post-training experiments, you will measure how different data sources, structures, and selection strategies affect capability, generalization, and alignment. You will own the full experiment loop: formulate the hypothesis, build the pipeline, run the model, analyze the results, identify confounders, and determine what should happen next. Working with partner labs and internal teams, you will turn our datasets into clear, defensible evidence: this data -> this improvement -> under these conditions. This is empirical, high-leverage work for someone who likes building quickly and extracting signal from noisy results.

RESPONSIBILITIES

- Design and build our post-training infrastructure for SFT, RL and evaluation workflows.

- Build reliable pipelines for data preparation, dataset versioning, sampling, training, checkpoint management, and evaluation.

- Develop experiment orchestration and tracking systems that make runs reproducible, comparable, and easy to debug.

- Create reusable abstractions that allow researchers to launch experiments quickly across datasets, models, and training recipes.

- Integrate our systems with partner-lab training stacks, model APIs, compute environments, and evaluation infrastructure.