About the role

Join a leading AI lab focused on quantum technology.
Work on production-grade LLM architectures and AI systems.
Collaborate with top scientists and engineers globally.
Lead initiatives in AI system design and implementation.
Foster a culture of technical excellence within teams.

Skills the posting asks for

Machine Learning
AI
LLM
Quantum Computing

Summarised from the employer’s posting, which is reproduced in full below.

The employer’s full posting

Join Qutwo - Europe's private Quantum AI Lab, on our quest to bridge the gap between the classical and the quantum world. We partner with the most advanced and forward-thinking customers, solving fundamental real-world problems, side by side.

The best scientists and engineers in the world are drawn to one thing: a problem worthy of their ability. At Qutwo, we have one. We are a European AI lab for the quantum era, co-founded by pioneers behind IQM and Silo AI, with a team spanning MIT, UC Berkeley, Cambridge, and Carnegie Mellon, and backed by the founders of Skype, Hugging Face, Quantinuum, Supercell and many more. We work at the intersection of quantum computing, AI, optimization, and simulation — where the answers do not yet exist and the work genuinely matters.

We are seeking exceptional AI/ML Engineers. If you are a humble learner, not afraid of the unknown, we encourage you to apply.

Role Description

As a Senior AI/ML Engineer, you will join our team in a full-time capacity, collaborating closely with our scientists, engineers, and customers. In this role, you will apply a blend of exploratory research and practical implementation to design production-grade LLM architectures and autonomous Agentic AI systems that solve complex, real-world problems and drive customer value.

Qualifications

Proven track record of designing, deploying, and maintaining end-to-end ML systems and production-grade LLM applications at scale. You have owned the transition from prototype to production-hardened service.
5+ years of professional experience in ML/AI Engineering. You prioritize robust system design, observability, safety/guardrails, and "infrastructure as code" over one-off experiments.
Hands-on experience architecting Agentic AI systems, including multi-agent coordination, stateful workflow orchestration, autonomous decision loops, and complex action execution.
Strong background in LLM integration, including function calling/tool-use, structured output parsing, and advanced reasoning strategies.
Exceptional ability to lead cross-functional initiatives, translate business objectives into technical roadmaps, and uphold high standards for code quality and system reliability.
Experience leveling up high-performing engineering teams, conducting deep-dive code/architecture reviews, and fostering a culture of technical excellence.

Experience in one or more of the following areas is a plus:

Deep experience in LLM infrastructure, including advanced RAG systems, model fine-tuning, agent memory architectures, and automated evaluation frameworks.
Proficiency with vector databases, distributed computing, and GPU optimization.
Familiarity with combinatorial optimization algorithms or the intersection of ML and Quantum Computing.