Senior Full-Stack ML Engineers

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 looking for Senior Full-Stack ML Engineers who can take machine learning from research code to a secure, running product — and who are comfortable working across the whole system rather than one layer of it.

Our product combines ML pipelines for deep neural network compression with small, security-critical SaaS services. That means one week you might be making a distributed training and compression pipeline reproducible and cost-efficient, and the next you might be designing an API, hardening a deployment, or building the UI that makes the whole thing usable.

We are not looking for someone who has used every tool in our stack. We are looking for engineers with enough architectural judgement and breadth to decide what should be built and why and enough fluency to review, debug, and take responsibility for the result.

Role Description

• Productizing ML pipelines for DNN compression: turning experimental training, compression, and evaluation workflows into pipelines that are reproducible, observable, and cost-efficient to run continuously.

• Designing and implementing backend services: Small, well-bounded services built to a high security standard from day one—primarily leveraging Python, but with room to evaluate and introduce the right tools for the job.

• Building the frontends that expose our pipelines and services to users.

• Owning deployment and operations of what you build: containers, Kubernetes, CI/CD, observability, and the security posture around it all.

• Making architectural decisions with the team and writing them down clearly enough that both your colleagues and our coding agents can act on them.

Requirements

• Minimum 7 years of experience building and running production software, with a track record of shipping systems you designed yourself.

• Genuine breadth: you have worked on backends, data or ML pipelines, and infrastructure, and you are not afraid of the parts of the stack you know least well.

• Solid grasp of software architecture and cloud platforms — you can describe a system's big picture, its boundaries, and its failure modes, and explain the trade-offs behind your choices.

• Practical ML fluency: Demonstrated experience training, fine-tuning, and running inference with PyTorch or TensorFlow, alongside a clear grasp of modern ML tooling and pipeline architecture.

• Proven ability to rigorously evaluate trade-offs between model size, latency, memory, and accuracy—acting as an authoritative technical counterpart to our research team.

• Comfort with AI-assisted development: Coding agents are a normal part of how we work; we expect you to use them well, to give them the context and constraints they need, and to read the resulting code critically and fluently.

• Security-first mindset: you think about trust boundaries, secrets, dependencies, and supply chain by default, not as a review step at the end.

• Fluent in English with a proven ability to thrive in agile, dynamic teams with fluid, cross-functional responsibilities.

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

• Solid experience in Frontend and UX experience

• Hands-on experience with model compression: quantization, pruning, distillation, sparsity, or inference-runtime optimization.

• Familiarity with quantum computing concepts at a high level, or curiosity about them.

• Experience meeting formal security or compliance requirements in a small company.

• Experience with distributed training, GPU scheduling, or ML cost optimization.