Research Engineer

About AfterQuery

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.

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.

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.

Required Qualifications

• 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.

Company Benefits (For Eligible Employees):

We comply with applicable pay transparency requirements and provide compensation ranges based on the position, qualifications, experience, and other relevant factors. Reasonable accommodations are available to qualified individuals with disabilities and for sincerely held religious beliefs, as required by law. This job description is intended to describe the general nature and level of work performed and is not an exhaustive list of all duties, responsibilities, qualifications, or working conditions associated with the position.