ML Data & Platform Engineer — Hybrid ML Ops & Pipelines

Speechmatics; Cambridge; Hybrid; GBP 70,000 - 110,000; Full time

Benefits offered by this job

Private Medical

Dental for you and your family

Global working opportunities

Generous holiday allowance

Pension/401K matching

Working from home allowance

Laptop and accessories of your choice

Parental leave support (adoption & Re‑

Job summary

Speechmatics is seeking an ML Data & Platform Engineer to own the data pipelines and the platform that trains, evaluates, and serves models in production. You will work across the full stack—from data infrastructure to production ML—owning problems end-to-end to accelerate model delivery.

You’ll collaborate with the ML team to improve data quality, observability, and MLOps practices, while scaling infrastructure for faster iteration and reliability.

Qualifications

• Strong proficiency in Python and SQL with backend/data engineering foundation.
• Hands-on experience with Docker, Kubernetes, and major cloud provider.
• Experience building data pipelines and ETL/ELT processes at scale, including web scraping or automated data collection.
• 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.

Responsibilities

• 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.

Skills

Python

SQL

Containerisation

Kubernetes

Cloud provider

Data pipelines

ETL/ELT

Web scraping

ML lifecycle

Observability

Distributed systems

MLOps

Self-starter

Tools

Docker

Kubernetes

Job description

Speechmatics is seeking an ML Data & Platform Engineer to own the data pipelines and the platform that trains, evaluates, and serves models in production. You will work across the full stack—from data infrastructure to production ML—owning problems end-to-end to accelerate model delivery.

You’ll collaborate with the ML team to improve data quality, observability, and MLOps practices, while scaling infrastructure for faster iteration and reliability.