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Riverlane

Quantum software platform for fault-tolerant control

AI/ML Engineer

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeDelft, NetherlandsHybrid

About the Job

Requirements

  • An MSc or PhD in Computer Science, Mathematics, Physics, or a related technical field, or equivalent professional experience.
  • At least 2–3 years’ experience in machine learning, deep learning, or other areas of artificial intelligence, gained in industry or academia, such as as a postdoctoral researcher.
  • Working knowledge of quantum error correction, gained through research, projects, or industry experience.
  • Strong programming skills, preferably in Python and/or C++, and good software engineering practices, including version control, testing, and code review.
  • Experience with scientific computing libraries such as NumPy and SciPy, and machine learning frameworks such as JAX or PyTorch.
  • A solid understanding of neural network architectures such as transformers, convolutional neural networks, and recurrent neural networks, and training techniques including supervised, unsupervised, and reinforcement learning.
  • Experience with machine learning model design and optimisation, including training, evaluation, fine-tuning, and interpretation.
  • Experience using GPU or TPU acceleration for training and evaluation.
  • Excellent communication and collaboration skills, including the ability to explain technical ideas clearly to colleagues from different backgrounds.

Responsibilities

  • Design, train, and evaluate machine learning models for quantum error correction tasks such as decoding, calibration, and noise characterisation, using simulated and real experimental data.
  • Build robust data pipelines and training infrastructure to support rapid experimentation and hyperparameter tuning.
  • Optimise models for performance, latency, and scalability, considering hardware and quantum error correction constraints.
  • Work with quantum error correction researchers to identify promising machine learning techniques and translate them into practical prototypes and algorithms.
  • Work closely with software, hardware, and product teams to ensure research is grounded in real-world constraints and to integrate prototyped models into Riverlane’s quantum error correction stack.
  • Communicate results through technical write-ups and discussions, and contribute to publications and conference presentations where appropriate.

Desired Qualifications

  • Experience with quantum error correction simulation and decoding tools such as Stim or PyMatching.
  • Understanding of one or more qubit modalities, such as superconducting, trapped-ion, neutral-atom, or photonic.
  • Experience with GPU-enabled SLURM clusters.
  • Knowledge of parallel programming, such as CUDA, or experience optimising models for low-latency, hardware-constrained deployment.
  • Experience applying machine learning to quantum error correction or other quantum computing problems, evidenced by publications or open-source contributions.

About the Company

Riverlane

Quantum software platform for fault-tolerant control

Riverlane builds software to make quantum computers practical. It develops Deltaflow.OS, a universal operating system that runs on quantum hardware and manages real-time error detection and correction, providing a common software layer across different hardware platforms. This enables control of quantum devices and coordination of error-correcting processes, a key step toward scalable, fault-tolerant quantum computers. Riverlane differentiates itself by focusing on quantum error correction and hardware-agnostic software infrastructure, positioning Deltaflow.OS at the core of the quantum computing stack. The company collaborates with hardware developers, national laboratories, and governments to accelerate the move from experimental quantum devices to reliable, scalable machines.

Company size
201-500
Company stage
Grant
Total funding
$131.5M
Headquarters
Cambridge, United Kingdom
Founded
2016

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