AfterQuery

Research Scientist - Post Training

Reposted 6 Days Ago

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San Francisco, CA, USA

In-Office

210K-450K Annually

Junior

Artificial Intelligence • Big Data

The Role

Design and run controlled SFT and RL post-training experiments to measure dataset impact on model capability, generalization, and alignment. Build public evaluations, publish research and reports, and iterate on data quality with internal teams and partner labs.

Summary Generated by Built In

About AfterQuery

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.

Why Apply

Massive Opportunity:
We are one of the fastest-growing YC companies in our batch, and we believe we can become one of the fastest-growing YC companies of all time.
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

Your job is to prove that our data works. You will design and run training experiments that isolate the impact of our datasets on model behavior. This includes SFT and RL-based post-training, where you’ll measure how different data sources shift capability, generalization, and alignment. Working closely with partner labs, you will turn our datasets into clear, defensible evidence: this data → this improvement → under these conditions. This is experimental, high-leverage work.

Responsibilities

Run controlled SFT and RL experiments to measure the impact of our datasets on model performance.

Help build public evals and new data types that push the frontier.

Publish external-facing research, blog posts, and technical reports.

Work with internal SPLs to iterate on data quality based on your results.

Required Qualifications

Strong familiarity with LLM training and evaluation methodologies.

Ability to design lightweight experiments, move fast, and extract actionable insights from messy results.

Comfort working across domains (you'll touch finance, software engineering, policy, and more).

A bias toward building over theorizing.

Preferred Qualifications

Great candidates are undergrad research or master's research (but haven't done a phd).

Genuine obsession with how data structure, selection, and quality drive model behavior.

Skills Required

Strong familiarity with LLM training and evaluation methodologies.
Deep understanding of how data structure, selection, and quality drive model behavior.
Ability to design lightweight experiments, move quickly, and extract actionable insights from messy results.
Comfort working across multiple domains (finance, software engineering, policy, etc.).
Bias toward building and applied experimentation rather than pure theory.
Undergraduate research or master's research experience (PhD not required).

AfterQuery Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about AfterQuery and has not been reviewed or approved by AfterQuery.

Fair & Transparent Compensation — Pay is considered attractive when work is accepted, with public role postings and materials indicating strong compensation across expert projects and core employee roles. The experts track also highlights transparent pay rates and approval-linked payouts that do land.
Healthcare Strength — Employee materials indicate medical, vision, and dental insurance are provided. Job postings reference a comprehensive package consistent with standard coverage.
Wellbeing & Lifestyle Benefits — Daily meal stipends, commute support via Uber credits, and a monthly wellness stipend (including gym membership coverage) are prominently advertised. These lifestyle perks suggest attention to day-to-day convenience and wellbeing.