Software Engineer - RL Environments

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 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.

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.