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
- Develop and implement machine learning solutions for underwater acoustic sensing use cases, including detection, classification, localization and anomaly monitoring within fiber-optic sensing systems.
- Select and adapt modelling approaches based on data availability, operational constraints and system requirements.
- 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.
- Design, train, evaluate and benchmark AI/ML models, ensuring robustness, generalization and deployment readiness.
- Validate models rigorously across laboratory experiments and offshore field datasets, with clear performance tracking and reproducible experimentation.
- Analyze and interpret large-scale experimental and real-world fiber-optic sensing datasets, contributing to scalable workflows for data preparation, labelling and augmentation.
- Support technical trade-offs between accuracy, interpretability, latency and real-time or embedded constraints.
- Collaborate closely with acoustics engineers, signal processing specialists, software and embedded teams to integrate ML components into operational acoustic sensing pipelines.
- Translate complex AI/ML results into clear technical insights that inform system-level engineering decisions.
- Contribute to strong engineering practices, including version-controlled experiments, documentation, traceability, validation standards and continuous improvement of ML components.
- 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.
- Competitive salary .
- Innovative high-tech, international organization .
- The opportunity to work in a cross-functional and interdisciplinary environment .
- A lot to learn and to develop, we stimulate personal development .
- Lunches.
Basic qualifications
- MSc in Machine Learning, Signal Processing, Applied Mathematics, Computer Science, Physics, or a related field.
- Strong foundation in linear algebra, probability, statistics, and signal processing.
- 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.
- Supervised and unsupervised learning approaches for detection and classification.
- Hands-on experience with model training, evaluation, and validation in applied settings.
- Proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow, JAX).
- Ability to work in an applied R&D environment with experimental and field data, balancing model performance with real-world constraints.
Preferred qualifications
- Experience with underwater acoustics, sonar, or beamforming-related applications.
- Knowledge of embedded or real-time deployment constraints.
- Experience with sensor fusion, self-supervised learning, or physics-informed ML.
- Familiarity with offshore, defense, or industrial monitoring domains.
- Familiarity with structured ML development processes such as CRISP-ML(Q) or similar lifecycle and quality frameworks.
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