Senior Machine Learning Systems Engineer

HUG are currently partnered with a well-funded, high-growth AI startup building advanced machine learning systems deployed in real-world production environments. They are hiring a Senior ML Systems Engineer to build and scale the infrastructure that enables cutting-edge ML models to move from research into production.

The Role

• This is a highly technical IC engineering role sitting at the intersection of ML systems, infrastructure, and large-scale data.
• You will be responsible for building the platforms and systems that allow applied scientists to train, evaluate, and deploy models efficiently at scale.
• This role is not research-focused, it is about making ML systems work reliably in production. You’ll operate across the full lifecycle, from data ingestion through to inference and optimisation.

What You’ll Be Doing

• Build and scale data platforms for large, complex datasets

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• Improve ML training infrastructure and data pipelines
• Develop tooling for dataset inspection, model evaluation, and experimentation
• Design systems for model versioning, lifecycle management, and deployment
• Optimise production inference pipelines and system performance across distributed/GPU environments
• Work closely with researchers to enable rapid experimentation and productionisation

What They’re Looking For

• 5+ years experience building production ML systems or ML infrastructure

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• Experience deploying ML models at scale or building platforms/tools for ML teams
• Strong Python experience
• Experience with a production language (e.g. C++, Java, Scala)
• Solid understanding of distributed systems
• Experience working with large-scale, high-volume datasets
• Experience in a startup or scale-up environment (ideally 50–300 people)
• Product-minded, able to balance technical depth with real-world impact

Nice to Have

• Experience with modern ML tooling (e.g. PyTorch, Ray, Triton, Spark, Iceberg)
• Background working with complex or non-standard data types
• Experience optimising performance across distributed or GPU systems
• Exposure to ML platform tooling for research teams

Logistics

• London (hybrid)
• £100k-£155k base + equity
• Visa sponsorship available