Machine Learning Engineer – Ship Design & Hydrodynamics

The Knowledge Transfer Network Limited; Greater London; Hybrid; GBP 60,000 - 90,000; Full time

Job summary

Compute Maritime Ltd, a London-based deep-tech company, seeks a Machine Learning Engineer to embed physics-informed generative AI tools within a marine vessel concept, generation and evaluation platform as part of a Knowledge Transfer Partnership (KTP) with the University of Southampton.

You'll translate research into commercially viable solutions, develop fast CFD-informed models predicting ship resistance, propulsion and energy efficiency; extend NeuralShipper for wind-assisted propulsion and

Qualifications

• Advanced degree in ML/CFD or related field.
• Experience applying ML to engineering or physical systems.
• Strong Python programming; C++ or MATLAB desirable.
• Familiarity with CFD tools and physics-informed modelling.

Responsibilities

• Translate research into commercially viable solutions by managing work packages.
• Develop and validate fast, physics-informed models for ship resistance, propulsion and energy efficiency using CFD and benchmark data.
• Design and implement multidisciplinary optimisation methods for NeuralShipper as scalable software tools.
• Extend NeuralShipper to wind-assisted propulsion and rigid sail systems with industry validation.

Skills

Python

C++

Deep Learning

Machine Learning

Physics-informed ML

CFD

Education

MSc/MEng or PhD in ML/CFD/Engineering

Tools

PyTorch

TensorFlow

JAX

STAR-CCM+

NumPy/SciPy

Job description

A Computational Ship Hydrodynamics and Design Optimisation specialist is required to work on an ambitious and novel project to embed physics informed generative AI tools within a marine vessel concept, generation and evaluation platform.

This will be part of a Knowledge Transfer Partnership (KTP), which is a collaborative project between Compute Maritime Ltd and the University of Southampton.

Duties and Responsibilities

• The Machine Learning Engineer will be required to undertake the following:
• Translate and embed research into commercially viable solution by managing a series of work packages.
• Develop and validate fast, physics-informed models for predicting ship resistance, propulsion performance and energy efficiency using CFD and benchmark data.
• Design and implement multidisciplinary optimisation methods, integrating them into NeuralShipper as robust and scalable software tools for automated vessel design improvement.
• Extend NeuralShipper’s capabilities to wind-assisted propulsion and rigid sail systems, working with industry stakeholders to validate the tools against practical design requirements.

Skills & Experience

The successful Machine Learning Engineer will have the following skills, experience and attributes:

• MSc/MEng or PhD (desirable) in Machine Learning, AI, Computational Fluid Dynamics, Hydrodynamics, Optimisation, or a related discipline.
• Experience of applying machine learning and deep learning to engineering or physical systems.
• Strong scientific programming skills in Python, with experience in C++, MATLAB, or similar languages desirable.
• Experience with a deep learning framework such as PyTorch, TensorFlow, or JAX (desirable).
• Experience with engineering simulation tools relevant to CFD, hydrodynamics, or vessel performance, such as STAR-CCM+.
• Understanding of naval architecture, ship hydrodynamics, vessel performance, or design analysis.
• Experience in physics-informed machine learning, surrogate modelling, generative AI, or design optimisation would be desirable.
• An entrepreneurial mindset and a willingness to build commercial acumen alongside technical strengths.

Through its proprietary technologies, most notably NeuralShipper, the company is building the first AI-native maritime design ecosystem, offering end-to-end solutions across the vessel lifecycle, from early concept design to operational optimisation.

About the business

Compute Maritime Ltd is a London-based deep-tech company bringing intelligence to the core of the global shipbuilding industry through generative artificial intelligence (AI) and high-performance computing.