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AI Engineer

Ocean Infinity
London, GBR
Full-time
Mid
Hybrid
GBP 85000-90000 yearly / year
Discovered Today
PythonPyTorchTensorFlowDINOJoint Embedding Predictive Architectures (JEPA)Kaggle
Free

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PythonPyTorchTensorFlow
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What you will do! 🤖

  • Contribute to the design and delivery of AI solutions, taking projects from exploratory research through to production-ready inference pipelines.
  • Define the technical approach for complex AI projects, making decisions on model architectures, data strategies, evaluation frameworks, and deployment paths.
  • Design and implement models for detection, classification, prediction, and feature extraction.
  • Work with large-scale, often unlabelled datasets and apply self-supervised and semi-supervised learning techniques to build robust models.
  • Adapt and fine-tune foundation models, with techniques such as active learning for domain specific challenges in maritime AI.
  • Review code, define best practices, and champion high engineering standards across the AI team.
  • Communicate technical progress, trade-offs, and results clearly to both technical and non technical stakeholders.

What we look for!🔍

  • You have a degree in Computer Science, Mathematics, or a related field. A PhD or research background in Machine Learning or underwater sensor processing algorithms is a strong plus.
  • You have evidence of hands-on experience in AI and Machine Learning.
  • You are a solid Python developer with strong experience in PyTorch, TensorFlow, or similar deep learning frameworks.
  • You have experience working with non-standard imagery data. Writing custom data loaders and curating custom datasets. • You write clean, maintainable code and hold software engineering quality to the same standard as model performance.
  • You are comfortable operating with high autonomy in ambiguous, evolving environments and can break large problems into deliverable increments.
  • You communicate effectively with both technical and non-technical stakeholders and can explain complex AI concepts clearly

Nice to have! ✨

• Experience with self-supervised learning approaches such as DINO-style training, or joint embedding predictive architectures (JEPA).

• Familiarity with foundation models and their adaptation for domain-specific tasks via fine-tuning or few-shot methods.

• Experience working with sonar, magnetometer, acoustic or other remote-sensing data types.

• Knowledge of MLOps practices including experiment tracking, model versioning, containerisation, and CI/CD pipelines.

• Publications or contributions to the research community through journals, conferences, Kaggle competitions, or open-source projects.

Benefits

  • *Salary £85,000 - £90,000 Per Annum*

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