Senior AI/ML Engineer

We are looking for an experienced AI/ML Engineer to develop and deploy machine learning solutions for real-world underwater acoustic sensing systems.

Optics11 is a deep-tech company developing cutting-edge fiber-optic sensing technologies for challenging environments, including underwater acoustics, offshore energy, and defense applications. Our systems enable high-performance sensing and monitoring through advanced photonics, signal processing, and data-driven intelligence.

We are expanding our R&D team with a Senior AI/ML Engineer who combines strong mathematical foundations with applied machine learning expertise to enhance the next generation of intelligent acoustic sensing solutions.

Role Overview:

As a Senior AI/ML Engineer, you will apply machine learning to real-world underwater acoustic sensing challenges using Optics11’s fiber-optic technology platforms. You will work closely with acoustics engineers, signal-processing specialists, and embedded/software teams to develop robust ML components that integrate seamlessly into operational systems.

This role requires a strong foundation in signal processing, applied mathematics, and statistics, and the ability to combine physics-based acoustic algorithms with data-driven ML methods.

Responsibilities:

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Develop and implement machine learning solutions for underwater acoustic sensing use cases, including detection, classification, localization and anomaly monitoring within fiber-optic sensing systems.

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Select and adapt modelling approaches based on data availability, operational constraints and system requirements.

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Apply strong foundations in applied mathematics, statistics and signal processing (e.g., beamforming, spectral analysis, detection pipelines) and integrate these with modern data-driven ML techniques.

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Design, train, evaluate and benchmark AI/ML models, ensuring robustness, generalization and deployment readiness.

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Validate models rigorously across laboratory experiments and offshore field datasets, with clear performance tracking and reproducible experimentation.

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Analyze and interpret large-scale experimental and real-world fiber-optic sensing datasets, contributing to scalable workflows for data preparation, labelling and augmentation.

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Support technical trade-offs between accuracy, interpretability, latency and real-time or embedded constraints.

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Collaborate closely with acoustics engineers, signal processing specialists, software and embedded teams to integrate ML components into operational acoustic sensing pipelines.

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Translate complex AI/ML results into clear technical insights that inform system-level engineering decisions.

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Contribute to strong engineering practices, including version-controlled experiments, documentation, traceability, validation standards and continuous improvement of ML components.

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Stay informed about advances in AI/ML for time-series analysis, sensor fusion and signal-informed learning, and prototype promising approaches that complement the broader technology stack.

What's in it for you?

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Competitive salary .

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Innovative high-tech, international organization .

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The opportunity to work in a cross-functional and interdisciplinary environment .

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A lot to learn and to develop, we stimulate personal development .

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Lunches.

Required Qualifications

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MSc in Machine Learning, Signal Processing, Applied Mathematics, Computer Science, Physics, or a related field.

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Strong foundation in linear algebra, probability, statistics, and signal processing.

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Proven experience applying AI/ML methods to time-series or sensor data, including techniques such as: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) (e.g., LSTMs), Transformer-based architectures for time-series learning.

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Supervised and unsupervised learning approaches for detection and classification.

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Hands-on experience with model training, evaluation, and validation in applied settings.

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Proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow, JAX).

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Ability to work in an applied R&D environment with experimental and field data, balancing model performance with real-world constraints.

Nice to Have

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Experience with underwater acoustics, sonar, or beamforming-related applications.

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Knowledge of embedded or real-time deployment constraints.

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Experience with sensor fusion, self-supervised learning, or physics-informed ML.

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Familiarity with offshore, defense, or industrial monitoring domains.

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Familiarity with structured ML development processes such as CRISP-ML(Q) or similar lifecycle and quality frameworks.

You'll be working with clients in the defense and security sectors, obtaining a Certificate of No Objection issued by the AIVD (Dutch General Intelligence and Security Service) is mandatory. This means you will need to undergo a security screening. For more information, please refer to details on security screening.

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