Research Scientist

About OpenRouter

OpenRouter is a premier AI routing and infrastructure platform enabling developers and enterprises to seamlessly access, manage, and optimize leading large language models (LLMs) from multiple providers. By eliminating vendor lock-in and overcoming capacity and cost challenges, OpenRouter allows top AI teams to innovate rapidly, scale effectively, and remain adaptable as models advance.

As AI adoption in enterprises grows quickly, OpenRouter plays a pivotal role in facilitating how organizations deploy LLMs across research, product development, and production environments.

Role Overview

We are seeking a Research Scientist to undertake profound, original research that enhances global understanding, assessment, and routing of large language models. You will engage with one of the most extensive AI datasets, comprising billions of LLM outputs covering diverse models, providers, and use cases.

In this role, you will lead your own research agenda by crafting experimental designs, creating evaluation frameworks, and generating insights that influence model comparison, selection, and deployment. Your research will guide OpenRouter’s routing intelligence, public rankings, and inform the broader AI community.

Success will be measured by the research's quality and impact rather than by producing production-level code or dashboards. Collaboration with product and engineering teams will occur, but the emphasis remains on depth and scientific rigor.

Key Responsibilities

• Develop and manage a research agenda targeting LLM evaluation, model performance, routing efficiencies, and AI usage analytics, delivering pioneering insights.
• Create innovative evaluation frameworks and benchmarking methodologies that move beyond conventional leaderboards, leveraging real-world generation data to assess models across various tasks and scenarios.
• Execute extensive empirical analyses on LLM behavior, assessing provider comparisons, temporal performance trends, and usage revealing model capabilities and limitations.
• Construct the statistical and mathematical models underpinning routing systems, designing heuristics that enable intelligent provider and model selection.
• Translate research discoveries into actionable enhancements for OpenRouter’s product and platform.
• Engage with external researchers, model vendors, and the open-source community to foster collective advancement in understanding LLM strengths and weaknesses.
• Collaborate closely with product and engineering teams to ensure that research outcomes inform platform improvements without being bound by production release schedules.

Required Qualifications and Skills

• Master’s or Doctorate in quantitative disciplines such as machine learning, statistics, computer science, mathematics, computational linguistics, or related fields.
• Proven record of original research evidenced by leading-author publications, notable open-source projects, or comparable impact within industry research settings.
• Expertise in statistics, experimental design, and causal inference, capable of designing rigorous studies with careful attention to validity, bias, and generalizability.
• Proficiency in Python programming for developing data pipelines, conducting large scale experiments, and prototyping models effectively.
• Strong knowledge of SQL and experience handling large analytical databases such as ClickHouse or BigQuery.
• Practical familiarity with contemporary ML and NLP methods including LLM evaluation, fine-tuning, embedding techniques, classification, and reinforcement learning from human feedback.
• Understanding of the current LLM ecosystem encompassing model architectures, provider platforms, benchmarking tools, and the comparative advantages and drawbacks of major models.

Professional Traits and Approach

• Highly inquisitive and self-motivated, capable of identifying key open research challenges and pursuing them proactively without external prompting.
• Balances scientific rigor with pragmatic application, maintaining high standards while adapting to the dynamic pace of a startup environment.
• Integrates AI tools such as LLMs and coding agents extensively within research workflows, possessing informed perspectives on their effective use.
• Excellent communication skills to clearly articulate complex research findings through various formats including publications, blog posts, internal documents, and discussions with non-technical stakeholders.
• Collaborative mindset, adept at working with cross-functional teams to convert research insights into practical product and engineering solutions.