Software Engineer - RL Environments

Department: Engineering

Location: San Francisco

Compensation: $180K – $220K • Offers Equity • $100K – $300K Bonus

Employment Type: FullTime

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 were 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

As a SWE (Environments), you will design the simulations, data, and evaluations that directly influence how frontier models learn. You'll work hands-on with research teams at top AI labs, experimenting with environment design, piloting novel data creation strategies, diagnosing model failure modes, and developing the metrics that determine whether a model is actually improving. You'll go from hypothesis to live experiment quickly, and your output will feed directly into model training runs at scale.
Day to day, you will design environments, tasks, and data that expose meaningful failure modes across domains like finance, code, and enterprise workflows. You will build and refine reward signals for various RL pipelines. You will develop quantitative frameworks for measuring dataset quality, diversity, and downstream impact on alignment and capability. You will partner with lab research teams to translate their training objectives into concrete data and evaluation specifications.

Responsibilities

Construct simulated worlds and explore data shapes that expose meaningful model failure modes across domains like finance, code, and enterprise workflows
Build and refine evaluation rubrics and reward signals for RLHF and RLVR training pipelines
Analyze agent-produced trajectories and run experiments to improve different model capabilities
Develop quantitative frameworks for measuring dataset quality, diversity, and downstream impact on model alignment and capability
Create and manage both real world & synthetic data pipelines
Partner with lab research teams to translate their training objectives into concrete data and evaluation specifications
Partner with in-house researchers to run post-training experiments and scale training infrastructure

Required Qualifications

Ability to design lightweight experiments, move fast, and extract actionable insights from messy results
Experience with using Docker, or similar containerization tools, to design and monitor systems at scale
Strong familiarity with common reinforcement learning algorithms and methods, especially with respect to post-training LLMs

Preferred Qualifications

Major plus if they've worked for/interned for any RL environment companies in the past or any AI safety or benchmarking orgs like METR, Artificial Analysis, etc.
Former founders and early engineers at early stage startups are a plus. We want people who can demonstrate they work hard, learn fast, and care deeply about getting the details right.