Research Scientist

About OpenRouter OpenRouter is the leading AI routing and infrastructure layer that developers and enterprises use to access, manage, and optimize the best large language models across providers without lock-in, capacity constraints, or unnecessary cost. We power the most advanced AI teams in the world by giving them the flexibility to move fast, scale confidently, and stay future-proof as models evolve. As enterprise adoption of AI accelerates, OpenRouter sits at the center of how organizations operationalize LLMs across research, product, and production workloads. The Role As a Research Scientist, you will conduct deep, original research that advances how the world understands, evaluates, and routes large language models. You'll work with one of the richest datasets in AI: billions of LLM generations spanning every major model, provider, and use case. You will own and pursue a research agenda: designing experiments, developing evaluation frameworks, and producing work that shapes how models are compared, selected, and deployed. Your findings will inform OpenRouter's routing intelligence, public rankings, and the broader AI discourse. Success in this role is measured by the quality and impact of your research, not by shipping production code or building dashboards. You'll collaborate with product and engineering teams, but your primary focus is depth and rigor. What You'll Do - Own and pursue a research agenda focused on LLM evaluation, model quality, routing optimization, and AI usage patterns, contributing original insights that advance the field. - Design novel evaluation frameworks and benchmarks that go beyond standard leaderboards, using real-world generation data to capture how models actually perform across tasks and contexts. - Conduct large-scale empirical studies on LLM behavior: how models compare across providers, how performance changes over time, and how usage patterns reveal strengths and weaknesses. - Develop the statistical and mathematical foundations behind our routing systems, building the models and heuristics that power intelligent provider and model selection. - Identify opportunities to apply research findings to feed back into OpenRouter's product and platform. - Collaborate with external researchers, model providers, and the open-source community to advance shared understanding of LLM capabilities and limitations. - Work with product and engineering teams to translate research findings into improvements to OpenRouter's platform, without being constrained to a shipping cadence. What You Bring Experience & Technical Skills - MS or PhD in a quantitative field (machine learning, statistics, computer science, mathematics, computational linguistics, or similar). - Track record of original research, demonstrated by first-author publications, significant open-source contributions, or equivalent impact in industry research. - Deep expertise in statistics, experimental design, and causal inference. You can design rigorous studies and reason carefully about validity, bias, and generalizability. - Strong programming skills in Python. You can build data pipelines, run large-scale experiments, and prototype models efficiently. - Proficiency in SQL for working with large-scale analytical databases (ClickHouse, BigQuery, or similar). - Hands-on experience with modern ML/NLP techniques such as LLM evaluation, fine-tuning, embeddings, classification, or reinforcement learning from human feedback. - Familiarity with the current LLM landscape: model architectures, provider ecosystems, benchmark suites, and the strengths and limitations of leading models. Mindset & Approach - Deeply curious and self-directed. You identify the most important open questions and pursue them without waiting for direction. - Rigorous but pragmatic. You hold yourself to high scientific standards while operating at startup speed. - AI-first in your own workflow. You use LLMs, coding agents, and modern AI tools heavily in your research process and have strong opinions about what works. - Strong communicator. You can explain complex findings clearly in papers, blog posts, internal memos, and conversations with non-technical stakeholders. - Collaborative. You work well with product and engineering teams and can translate research insights into actionable recommendations.