Senior Machine Learning Engineer

G10X; Greater London; On-site; GBP 90,000 - 130,000; Full time

Job summary

G10X is seeking an experienced machine learning engineer to design and deploy production-grade ML systems in our London-based teams. You will build end-to-end pipelines, implement scalable training and inference, and collaborate with data scientists to turn models into value.

You should be proficient in Python and ML frameworks (PyTorch, TensorFlow), with experience in PySpark/Databricks, multimodal models, and modern MLOps practices. Strong communication and pragmatic delivery are essential.

Qualifications

• Significant experience designing and deploying machine learning systems in production environments.
• Strong software engineering skills in Python, with experience building maintainable, tested, and production-quality code.
• Strong experience with large-scale data processing using technologies such as PySpark and Databricks.
• Experience designing and building ML pipelines across the full lifecycle, from data preparation and model development through to deployment and monitoring.
• Experience developing deep learning models using frameworks such as PyTorch, TensorFlow, or similar.
• Experience with multimodal machine learning, representation learning, or embedding models, combining data sources such as images, text, structured metadata, or behavioural signals.
• Strong understanding of modern deep learning architectures, particularly Transformers, foundation models, multimodal learning, and representation learning techniques.
• Experience working with distributed computing, large datasets, and scalable model training or inference systems.
• Familiarity with cloud platforms and modern MLOps practices.
• Strong communication skills and the ability to collaborate effectively with scientists and engineers.

Skills

ML systems design

Python

Distributed data processing

ML pipelines

DL frameworks

PyTorch

TensorFlow

Multimodal models

Transformers

MLOps

Tools

PySpark

Databricks

Job description

• Significant experience designing and deploying machine learning systems in production environments.
• Strong software engineering skills in Python, with experience building maintainable, tested, and production-quality code.
• Strong experience with large-scale data processing using technologies such as PySpark and Databricks.
• Experience designing and building ML pipelines across the full lifecycle, from data preparation and model development through to deployment and monitoring.
• Experience developing deep learning models using frameworks such as PyTorch, TensorFlow, or similar.
• Experience with multimodal machine learning, representation learning, or embedding models, combining data sources such as images, text, structured metadata, or behavioural signals.
• Strong understanding of modern deep learning architectures, particularly Transformers, foundation models, multimodal learning, and representation learning techniques.
• Experience working with distributed computing, large datasets, and scalable model training or inference systems.
• Familiarity with cloud platforms and modern MLOps practices.
• Strong communication skills and the ability to collaborate effectively with scientists and engineers.
• A pragmatic mindset, balancing technical excellence with delivering business value.