openrouter - Research Scientist
Requirements
• 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.
Responsibilities
• 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.
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