AI Engineering Intern
EuropeRemoteFull-time
AI Summary
Designs, tests, and deploys AI-powered systems at scale, leading data and inference pipelines and integrating LLM-based components such as retrieval-augmented generation for enterprise applications.
About this role
AI engineers at QuantCo build cutting-edge systems that bridge the gap between advances in frontier model technology and real-world impact in the world's foremost companies. They design, build, and implement applied AI systems that improve the quality of human decisions and unlock major efficiencies. AI engineers work with data scientists to solve complex problems in inference optimization, scalable systems design, and human-computer interaction. Team members may work in forward-deployed roles, collaborating directly with clients to discover, rapidly iterate on and deploy AI solutions, as well as in centralized roles that abstract successful solutions into scalable assets. Our team combines technical deep learning and engineering expertise with an entrepreneurial mindset driven by rapid experimentation, first-principles thinking, and a passion for tackling complex real-world problems to deliver lasting impact.
Description
You will design, test, and deploy AI systems that are embedded at scale. Operating at the intersection of machine learning and engineering, you will leverage cutting-edge models to build robust, production-ready AI applications. You will lead the design of data and inference pipelines, orchestrate the integration of LLM-based components such as retrieval-augmented generation (RAG), and balance engineering constraints with business priorities. From system design to deployment, you will own all stages of product iteration and development, building solutions that seamlessly integrate into enterprise settings, designed to change the way our clients work with AI.
Requirements
Skills
API Integrations (Gemini, OpenAI, Anthropic)Cloud InfrastructureComputer VisionData StructuresDeep LearningDistributed SystemsGraph Neural NetworksHigh-performance ComputingInference PipelinesLLM Inference ConceptsMachine LearningNatural Language ProcessingProduction SystemsPrompt Engineering / CachingPythonRAG (retrieval-augmented Generation)Software Engineering FundamentalsStructured OutputsTool-calling
