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Job description

ML ENGINEER (Mid–Senior)

Architecture | Digital Innovation Unit

Full-time | Confidential client (50,000-person global engineering group)

A 50,000-person engineering organisation is expanding its Architecture Digital Innovation Unit and hiring an ML Engineer to build, train, and deploy machine learning systems for real architectural challenges. This is a hands-on role for someone who can take open-ended design problems, turn them into ML-solvable formulations, and ship models into prototypes and internal tools.

What you’ll do

Design, develop, and fine-tune ML models for architectural applications (generative floor plans, structural optimisation, energy performance prediction, and more)
Build and maintain end-to-end ML pipelines for training, evaluation, and inference
Work with Computational Design to prepare, clean, and validate training datasets
Integrate models into prototypes, internal tools, and existing design software workflows
Evaluate state-of-the-art approaches (diffusion models, transformers, GNNs) and apply what’s relevant to AEC use cases
Document experiments, results, and technical decisions clearly for cross-disciplinary audiences
Optimise models for performance, scalability, and real-world deployment
Stay current with AI research and translate practical advances into the team roadmap

What we’re looking for

4–8 years in ML/AI engineering (mid-to-senior, hands-on)
Strong Python plus PyTorch or TensorFlow
Solid understanding of supervised, unsupervised, and generative ML techniques
Experience deploying models (REST APIs, Docker, and cloud such as AWS/GCP/Azure)
Familiarity with computer vision and/or geometric deep learning (point clouds, meshes, graphs)
Strong software engineering fundamentals: Git, testing, modular code, reproducible builds
Confident communicator: able to explain technical concepts to non-technical stakeholders
Bachelor’s or Master’s in CS, Data Science, or a related field (or equivalent experience)

Nice to have

Exposure to AEC tools or spatial formats (IFC, RVT, OBJ, etc.)
Experience with GANs, diffusion models, VAEs
Grasshopper scripting and/or Dynamo for parametric design

Skills

We’re looking for an ML Engineer who’s equal parts builder and problem-solver — someone who can take messy, open-ended design challenges and translate them into clear ML objectives, datasets, and deployable systems. You should be strong in Python and comfortable working day-to-day in PyTorch or TensorFlow, with a practical grasp of supervised, unsupervised, and generative approaches (and the judgement to pick the simplest method that will work, not the fanciest one).

You’ll bring solid engineering habits: clean, modular code, version control, testing, and reproducible pipelines. You’re comfortable owning the full lifecycle — data preparation with cross-functional partners, training and evaluation, and then packaging models for real use via APIs, Docker, and cloud deployment. Experience with computer vision and/or geometric deep learning is important, ideally with an understanding of spatial data (meshes, point clouds, graphs) and how model choices impact performance and scalability in production.

Just as importantly, you can communicate well across disciplines. You can document experiments and technical decisions clearly, explain trade-offs to non-technical stakeholders, and collaborate closely with computational designers and architects to ensure what you build is usable, reliable, and aligned to real-world workflows. If you’ve worked with AEC tooling or formats (IFC/RVT/OBJ), or have hands-on experience with diffusion models, GANs/VAEs, Grasshopper, or Dynamo, that’s a strong plus.

This job post has been translated by AI and may contain minor differences or errors.

Preferred candidate

Residence location

All GCC Countries; India

Career level

Mid career

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