Teams at AfterQuery

Design and build post-training infrastructure for supervised fine-tuning, reinforcement learning, and model evaluation. Develop reproducible pipelines for data preparation, versioning, sampling, training, checkpointing, and experiment tracking. Integrate systems with partner training stacks, model APIs, compute environments, and evaluation platforms. Run controlled experiments to measure dataset impact on model behavior, analyze noisy results, identify confounders, and produce actionable conclusions.

Own planning and execution of technical research programs spanning post-training, enterprise initiatives, fellowships, evaluations, and partner research. Translate ambiguous objectives into milestones, owners, dependencies, success criteria, and decision points. Track experiment progress, risks, blockers, partner dependencies, and releases while aligning researchers, engineers, program leads, and external partners. Apply working knowledge of LLM development, datasets, supervised fine-tuning, reinforcement-learning post-training, and evaluation.

Own expert support for a frontier-AI expert network: manage queues and SLAs, resolve payment and project disputes, document and surface trends, build playbooks and escalation policies, and partner with operations and growth to reduce support volume and improve contributor experience.