AI Engineer

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

• BSc, MSc or PhD in Computer Science, Engineering, Physics, or a related technical or STEM field from a leading university. 
• Experience working with relevant models and/ or architectures within the following fields: Computer Vision, Natural Language Processing, or Graph Neural Networks.
• Experience writing production-ready code.
• Experience programming in Python. 
• Familiarity with the frontier labs APIs (Gemini, OpenAI, Anthropic), and their advanced features like structured outputs and tool calling.
• Understanding of LLM inference considerations e.g. input vs. output tokens, prompt caching.
• Strong computer science fundamentals in areas such as data structures, cloud infrastructure, distributed systems, machine learning, and high-performance computing.
• Experience and willingness to work through the challenges associated with large, unstructured, real-world data sets.
• Ability to develop custom tooling for efficient experimentation and evaluation of LLM applications / agentic workflows What We Offer You will be part of a smart, motivated, and driven team that revels in working on challenging problems and finding novel solutions; values our customers and users; and strives to deliver thoughtful and impactful products. You will be surrounded by people who respect each other and the contributions each of us brings.   We offer a generous compensation package that includes a base salary, bonus, stock options, and relocation support, along with additional location-based benefits.
  About QuantCo At QuantCo, we leverage expertise in data science, engineering, and economics to help organizations turn data into decisions. Started by 4 PhDs from Harvard and Stanford, we are now more than 180 professionals with extensive quantitative, engineering, and business experience. We are globally distributed with offices in Berlin, Boston, Cologne, Karlsruhe, London, Munich, San Francisco, and Zurich.   Our solutions include algorithmic pricing, data-driven claims management, and high-dimensional forecasting systems. Our customers include some of the largest financial, retail, and healthcare organizations in the US and Europe. We impact core business processes and decisions by combining advanced data-driven insights with scalable engineering solutions.   We are excited about writing mission-critical code that affects millions of people and impacts billions of dollars. If you feel the same way, we look forward to hearing from you.