ML Data & Platform Engineer

In this role you own the data and platform backbone for our speech AI models, building scalable pipelines for data ingestion, transformation and model training. You will support cross-functional ML work, focusing on trustable data, efficient training, and reliable serving in production. You’ll reduce friction across the ML lifecycle and help scale our platform as we grow. The position offers hands-on ownership in a collaborative, hybrid environment with opportunities to influence our data strategy and MLOps practices.

• flexible working
• private medical and dental
• global working opportunities
• generous holiday allowance
• pension/401K matching
• home office equipment allowance

• Design, build, and maintain scalable data pipelines for ingesting, transforming, validating, and storing large training datasets
• Develop and maintain data acquisition solutions (web scraping) to keep datasets fresh and high-quality at scale
• Build and operate infrastructure enabling rapid model deployment, evaluation, and production serving
• Optimize infrastructure for fast iteration and production reliability (GPU utilisation, scheduling, training efficiency)
• Implement observability across data pipelines and ML systems (monitoring, logging, alerts)
• Troubleshoot complex issues across distributed data, training, and inference systems
• Continuously improve data and MLOps practices and help shape the platform roadmap as we scale

• Strong proficiency in Python and SQL
• Hands-on experience with containerisation and orchestration (Docker, Kubernetes) and major cloud provider
• Experience building data pipelines and ETL/ELT at scale including web scraping or automated data collection
• Solid understanding of the ML lifecycle from data to training, evaluation, and serving
• Experience with data quality practices and production-grade observability
• Ability to design resilient, scalable architectures and operate distributed systems
• MLOps experience (model serving, experiment tracking, GPU/distributed training optimisation, reproducible ML workflows)
• Self-starter mentality with ability to identify problems and drive fixes without detailed direction

• self-starter
• ownership mindset
• problem-solving and proactive troubleshooting
• Python
• SQL
• Docker