ML Data & Platform Engineer

We're looking for an ML Data & Platform Engineer to own the infrastructure that powers our speech AI models: the pipelines that source and prepare training data, and the platform that trains, evaluates, and serves them in production. Speech AI has a data problem most ML teams don't, and you'll be at the centre of solving it, working as part of our ML team to remove friction across the entire lifecycle and get better models into production faster.

This is a broad, cross-functional role suited to someone who enjoys working across the full stack: data infrastructure, distributed systems, and production ML, and who takes ownership of problems end to end rather than waiting to be told what to fix.

What you'll do

• Designing, building, and maintaining scalable data pipelines for ingesting, transforming, validating, and storing large datasets used to train our models
• Developing and maintaining web scraping and data acquisition solutions to keep training datasets fresh, high-quality, and available at scale
• Building and operating the infrastructure that lets the ML team deploy and evaluate new models quickly, and that serves models efficiently and reliably in production
• Optimising infrastructure for both iteration speed and production reliability, including GPU utilisation, job scheduling, and training efficiency
• Implementing observability (monitoring, logging, alerting) across data pipelines and ML systems to catch issues early and keep things running smoothly
• Troubleshooting complex issues across distributed systems, spanning data infrastructure, training, and inference
• Continuously improving our data and MLOps practices, and helping shape the roadmap for how our platform evolves as we scale

What you'll need

• Strong proficiency in Python and SQL, with a solid backend or data engineering foundation
• Hands-on experience with containerisation and orchestration (Docker, Kubernetes), and working with a major cloud provider
• Experience building data pipelines and ETL/ELT processes at scale, including web scraping or automated data collection
• A solid understanding of the ML lifecycle, from data through to model training, evaluation, and serving
• Experience with data quality practices (validation, cleaning, normalisation) and/or production-grade observability
• Ability to design resilient, scalable architectures, and comfort operating and troubleshooting distributed systems
• MLOps experience, for example model serving, experiment tracking, GPU/distributed training optimisation, or reproducible ML workflows
• A self-starter mentality: comfortable identifying problems and driving fixes without needing detailed direction