RL Environments Engineer Summer Intern

**Location:** San Francisco preferred, remote considered **Duration:** 10-12 weeks, Summer 2026 **Compensation:** Paid internship ### **About Us** Preference Model is building the next generation of training data to power the future of AI. Today's models are powerful but fail to reach their potential across diverse use cases because so many of the tasks that we want to use these models are out of distribution. Preference Model creates RL environments where models encounter research and engineering problems, iterate, and learn from realistic feedback loops. Our founding team has previous experience on Anthropic’s data team building data infrastructure, tokenizers, and datasets behind the Claude. We are partnering with leading AI labs to push AI closer to achieving its transformative potential. We are backed by A16Z. ### **About the Role** We're looking for PhD students and gifted undergrads to spend the summer building RL training environments for large language models. ### **What you'll do** * Design and build RL environments that test LLM reasoning on ML, systems, and research problems * Write clean, production-grade Python (not notebooks) * Work with Docker, build reproducible environments, debug when things break * Translate ML papers and concepts into concrete training tasks ### **Who we're looking for** You're an undergrad or PhD student in CS, ML, math, physics, or a related field. You write real code, not just research prototypes. You read ML papers for fun in your free time. ### **Must have:** * Strong Python skills * Familiarity with how LLMs work, what they're good at, and where they fall short * Ability to work independently, take feedback, and iterate fast ### **Any of these would make you stand out:** * You understand transformer internals and have worked with training or inference code * You've written CUDA kernels or worked with low-level GPU programming * You have a research area you know deeply (publications, public code, or strong coursework) * You read broadly across ML and can connect ideas from different subfields * You've built interactive environments, simulations, or complex software systems ### **How to apply** Send your resume and a short note (2-3 sentences is fine) about what area of ML you're most interested in and why. Links to code, papers, or projects are more useful than a long cover letter.