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# Software Engineer, RL Environments

- **Company:** David Joseph & Company
- **Location:** San Francisco, CA, United States
- **Salary:** $180,000.0 - $220,000.0
- **Contract:** Permanent contract
- **Skills:** Code Generation, Data Structures, Software Safety, Software Engineering, Data Pipelines
- **Published:** July 13, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=dd67c39042388571

## About the Role

* 1-4 years of software engineering experience with strong technical depth
 * Design targeted data slices that surface model failure modes across high-stakes domains (finance, code generation, enterprise workflows)
 * Build and iterate on evaluation rubrics and reward signals powering RLHF and RLVR training pipelines
 * Develop quantitative frameworks to measure dataset quality, diversity, and downstream impact on model alignment and capability
 * Own end-to-end real world and synthetic data pipelines, from scoping with research teams to production-ready evaluation specs
 * Run annotator modeling experiments to improve model capabilities across task types

Green Flags

 * Experience at RL environment companies
 * Background in AI safety or benchmarking organizations like METR or Artificial Analysis
 * Genuine obsession with how data structure, selection, and quality drive model behavior
 * Ability to design lightweight experiments and move fast
 * Former founders or early engineers at early stage startups
 * Demonstrated ability to work hard, learn fast, and care deeply about details, * Looking for standard product engineering work - the real scope is data pipelines, reward modeling, and eval infra

## Description

As a SWE (Environments), you'll design the datasets and evaluation rubrics that directly influence how frontier models learn - going from hypothesis to live experiment quickly, with output feeding directly into model training runs at scale.

What you'll be doing

 * Design data slices 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
 * Model annotator behavior 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 and synthetic data pipelines
 * Partner with lab research teams to translate their training objectives into concrete data and evaluation specifications


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