Staff Machine Learning Engineer in Nationwide
Role details
Job location
Tech stack
Job description
This is a senior hands-on leadership position where you'll own the execution of the company's machine learning platform, working closely with research, software engineering and product teams to build scalable, production-grade AI solutions.
Responsibilities
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Own the end-to-end machine learning lifecycle, including data pipelines, model training, evaluation, inference and deployment.
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Fine-tune and optimise large models using modern techniques such as LoRA, QLoRA, supervised fine-tuning, preference optimisation and model distillation.
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Design scalable inference infrastructure with a focus on latency, reliability and operational efficiency.
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Build and maintain high-quality data pipelines for both synthetic and real-world datasets.
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Develop robust evaluation frameworks covering model quality, robustness, safety and performance.
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Optimise GPU utilisation, memory usage and inference performance for production deployments.
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Work closely with software engineers to integrate ML capabilities into backend services and customer-facing products.
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Lead technical delivery, mentor engineers and help shape engineering best practices.
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Balance research ambitions with practical engineering trade-offs to deliver value quickly.
Technology
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Python
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PyTorch and/or JAX
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Large-scale GPU training and inference infrastructure
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Modern ML deployment pipelines
Requirements
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Proven experience delivering machine learning systems into production.
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Strong understanding of modern foundation models and large models.
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Experience building scalable training and inference infrastructure.
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Excellent software engineering skills with a focus on maintainable, production-quality code.
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Comfortable leading technical delivery while remaining hands-on.
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Strong communication skills and the ability to collaborate across research, engineering and product teams.