About the role#
We are looking for a Research Intern to join our Agent RL Training team. You will work with a full-time mentor to explore how to apply large language models to our core business. This includes content understanding, recommendation, agentic web browsing, and autonomous task completion. This is a hands-on role where you will drive experiments, propose ideas, and iterate quickly.
What you'll do#
- Collaborate with your mentor to identify high-impact research directions for applying LLMs to our products.
- Run end-to-end SFT experiments on LLM-based agents independently.
- Assist with RL-related exploration, including reward design and training iteration.
- Curate and build high-quality training datasets, such as instruction-following data, preference pairs, agent trajectories, and synthetic data.
- Contribute to public publications, with support for top-venue submissions.
What you'll need#
- Strong Python and PyTorch skills.
- Independent capability in end-to-end model SFT and a basic understanding of RL-based post-training methods like RLHF, DPO, PPO, or GRPO.
- Excellent taste in model behavior, with the ability to reason about and articulate what good looks like across user-facing domains.
- A genuine interest in research, such as reading papers for fun or tinkering with models.
- High motivation and commitment to push projects across the finish line.
- Pursuing a degree in Computer Science, Data Science, or Mathematics.
- Preferred: Publications at top-tier venues, experience with multi-node distributed training, proficiency in writing custom GPU kernels, experience building synthetic data pipelines, or familiarity with open-source RL frameworks.
Location & details#
- This is a full-time, on-site role based in Mountain View, California.
- The position is paid.
- We offer a rolling internship term.
- We do not provide visa sponsorship for this position.
About NewsBreak
NewsBreak operates a digital platform that aggregates local news and community information. The company connects users with content from over 10,000 sources across the United States. It maintains a focus on machine learning and natural language processing to personalize information delivery. Founded in 2015, the organization is based in Mountain View, California.
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