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AfterQuery is an applied research lab curating data solutions for foundation model development, serving frontier AI labs with the mission of delivering the best data to power the best models. The company is YC's fastest unicorn, valued at $3.2 billion, backed by leading investors including Altos Ventures, BoxGroup, Y Combinator, and angels from Google DeepMind, OpenAI, Anthropic, Meta Superintelligence Labs, and Microsoft AI.
As Research Engineer, you will design and run training experiments that isolate the impact of datasets on model behavior. Through controlled SFT (Supervised Fine-Tuning) and RL (Reinforcement Learning) 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 hypotheses, build pipelines, run models, analyze results, identify confounders, and determine next steps. Working with partner labs and internal teams, you will turn datasets into clear, defensible evidence showing data impact under specific conditions. This is empirical, high-leverage work for someone who builds quickly and extracts signal from noisy results.
Key responsibilities include designing and building post-training infrastructure for SFT, RL, and evaluation workflows; building reliable pipelines for data preparation, dataset versioning, sampling, training, checkpoint management, and evaluation; developing experiment orchestration and tracking systems that make runs reproducible, comparable, and easy to debug; creating reusable abstractions that allow researchers to launch experiments quickly across datasets, models, and training recipes; and integrating systems with partner-lab training stacks, model APIs, compute environments, and evaluation infrastructure.
REQUIREMENTS:
- At least 2 years of professional experience in machine learning engineering, research engineering, ML infrastructure, or 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 ability to extract actionable conclusions from noisy results
- Ability to move quickly between infrastructure engineering and hands-on experimentation
- Bias toward building, testing, and shipping
