AI Research Engineer Intern

Company Description OrcaRouter.ai is a unified AI inference gateway and open AI infrastructure platform that gives developers access to 200+ models from providers such as OpenAI, Anthropic, Gemini, DeepSeek, Qwen, Kimi, and xAI through a single API key. The platform offers adaptive routing, fallback, unified billing, bring-your-own-key (BYOK), and zero markup, supporting both OpenAI-compatible APIs and native Anthropic and Gemini endpoints. Beyond the hosted gateway, OrcaRouter.ai builds in the open, including self-hosted routing infrastructure, MCP tooling, SDK integrations, and open-weight model releases and research. The team actively shares work and resources across X, Hugging Face, Ollama, GitHub, arXiv, and comprehensive product documentation, creating a developer-focused ecosystem for modern AI workloads.Role Description The AI Research Engineer Intern role is a full-time remote position focused on advancing OrcaRouter.ai’s routing, inference, and open-weight model capabilities. Day-to-day responsibilities include designing and implementing experiments in pattern recognition and neural networks, developing and evaluating models for natural language processing tasks, and contributing to the research behind adaptive routing and multi-model orchestration. The intern will prototype and maintain software components in Python or similar languages, integrate models and APIs into the OrcaRouter.ai stack, and help build tools, demos, and benchmarks that showcase platform capabilities. The role involves reading and summarizing recent AI research, collaborating with engineering and research teams on experimental design, and documenting findings, metrics, and best practices for internal and public-facing technical content.QualificationsStrong foundation in Computer Science and Software Development, including data structures, algorithms, and experience with languages such as Python.Knowledge of Pattern Recognition and Neural Networks, with experience training, evaluating, or fine-tuning machine learning models.Familiarity with Natural Language Processing (NLP), including working with embeddings, sequence models, or large language models.Currently pursuing or recently completed a degree in Computer Science, Electrical Engineering, Mathematics, or a related quantitative field.Experience with machine learning frameworks (e.g., PyTorch, TensorFlow, JAX) and modern AI tooling and APIs is highly beneficial.Comfort working with Git, Linux-based development environments, and cloud or containerized workflows (e.g., Docker, Kubernetes) is a plus.Ability to design experiments, analyze results, and communicate technical concepts clearly in written and verbal form.Self-directed, able to work effectively in a remote, collaborative environment, with interest in open-source and open research.