Research Engineer

AfterQuery•San Francisco, CA

49d

About The Position

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. Overview 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.

Requirements

At least 2 years of professional experience in machine learning engineering, research engineering, ML infrastructure, or a closely related field; 2-4+ years preferred.
Strong Python and software-engineering skills.
Hands on experience with PyTorch, JAX, Ray, and Slurm.
Experience building production-quality ML training, evaluation, or data infrastructure.
HAnds-on experience with LLM fine-tuning, post-training, and evaluation.
Ability to build reliable, reproducible systems for launching and comparing ML experiments.
Strong debugging skills across distributed systems, data pipelines, training infrastructure, and model behavior.
Understanding of experimental design and the ability to extract actionable conclusions from noisy results.
Ability to move quickly between infrastructure engineering and hands-on experimentation.
A bias toward building, testing, and shipping.

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.