Machine Learning Engineer

Lead Machine Learning Engineer – Sail Performance

📍 Location: Hybrid (UK, France, Italy, Spain, Netherlands)

💼 Employment Type: Full-time, Permanent

🎯 Level: Technical Lead / Senior Engineer

Company Overview

North Sails is the global leader in sailmaking, designing and manufacturing sails for premier racing and cruising applications worldwide, including the America’s Cup, the Ocean Race, and Grand Prix racing circuits. Design and production operations are integrated across international hubs.

Program Overview

Sail design presents a complex coupled fluid-structural challenge. A flexible sail deforms under aerodynamic loading, causing its operational flying shape to differ significantly from its manufactured geometry. To evaluate high-dimensional design spaces at speed, machine learning surrogate models are being integrated directly into the design pipeline. The primary objective is developing surrogate models capable of rapidly predicting integrated aerodynamic forces, moments, and surface pressure distributions for user-defined flying shapes and operational conditions.

Position Summary

The Lead Machine Learning Engineer will oversee technical delivery of the aerodynamic surrogate modeling initiative, holding primary responsibility for model architecture selection, input feature representation, training strategy, and validation rigor. The role leads technical work across a multidisciplinary engineering team (comprising Machine Learning Engineers and CFD specialists) and coordinates technical scope delivered by external specialized partners. Initial deliverables focus on deploying a production-ready aerodynamic surrogate model into active design workflows, with long-term expansion into structural and coupled fluid-structure interaction (FSI) domains.

Key Responsibilities

• Define and implement surrogate modeling architectures, input representations, and training methodologies tailored to 3D field data.
• Formulate multi-fidelity learning frameworks utilizing fast panel solver data alongside high-fidelity RANS CFD datasets.
• Establish validation protocols, out-of-distribution evaluation metrics, and uncertainty quantification standards for automated design searches.
• Collaborate directly with senior sail designers to align model performance metrics with practical design decision criteria.
• Direct computational fluid dynamics (CFD) data generation requirements and campaign execution alongside CFD specialists.
• Provide technical oversight, architectural guidance, and professional mentorship across the machine learning engineering team.
• Manage external partner deliverables, maintaining technical governance and quality control standards.
• Oversee long-term technical roadmap development for structural modeling and coupled fluid-structure interaction.

Qualifications & Requirements

• Extensive background developing and deploying ML surrogate or emulator models for physical simulation applications into production environments.
• Proven literacy in Computational Fluid Dynamics (CFD), including solver mechanics, numerical convergence criteria, and flow physics.
• Demonstrated proficiency with 3D field-based and geometric deep learning architectures (e.g., Graph Neural Networks, Neural Operators, Mesh-based methods, Point Cloud models).
• Advanced Python capability and expertise with modern deep learning frameworks (PyTorch or TensorFlow), adhering to reproducible software engineering standards.
• Track record of technical leadership, workstream ownership, and clear technical communication.

Good to Have (Desirable)

• Practical experience with multi-fidelity learning, transfer learning, or co-kriging approaches.
• Exposure to Finite Element Analysis (FEA), structural mechanics, or coupled fluid-structure interaction (FSI).
• Experience managing external technical vendors or research consultants.
• Domain familiarity with marine engineering, naval architecture, or aerodynamics.

Working Arrangements & Employment Conditions

This position operates on a remote-first model with eligible employment locations in the United Kingdom, France, Italy, Spain, or the Netherlands. Periodic travel is required for engineering and design integration reviews. Compensation is aligned with regional market benchmarks in the country of employment.