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

We encourage you to apply even if you do not feel you match all of the requirements exactly. The list of requirements is intended to show the kinds of experience and qualities we’re looking for, but it is not exhaustive. If you are interested in the role, the team, and our mission, we would love to consider your application. We are always open to conversations and look forward to hearing from you.

Who we are:

Speechmatics is the leading expert in Speech Intelligence, and uses AI and Machine Learning to unlock business value in human speech worldwide. We work with an amazing mix of global companies, and our technology can integrate into our customers stack irrespective of their industry or use case – making it the go-to solution to harness useful information from speech.

Joining us means working with some of the smartest minds around the world, focused on cutting-edge projects and deploying the latest techniques to disrupt the market. We believe in putting people first; we’ll do all we can to help you develop your skills and give you the tools you need to thrive. Our Focus Fridays give you an undisturbed day of focus, offset with Together Tuesdays when we have our team meetings, so you've always got the right balance.

We have structured a hybrid approach that includes 2-3 designated office …