ML Data & Platform Engineer - Hybrid, Pipelines & MLOps

Speechmatics; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Benefits offered by this job

Flexible working

Private Medical

Dental for you and family

Global opportunities

Generous holiday allowance

Job summary

Speechmatics is seeking an ML Data & Platform Engineer to own the infrastructure powering our speech AI models. You will design data pipelines, implement data acquisition at scale, and operate the platform that trains, evaluates, and serves models in production.

You'll work on observability, scalable architectures, and GPU-optimised training while collaborating with the ML team. We offer flexible working with 2–3 in-office days per week in London and opportunities to grow.

Qualifications

• Strong Python and SQL skills with backend/data engineering foundation.
• Experience building data pipelines and ETL/ELT at scale.
• Knowledge of ML lifecycle from data to training and serving.
• Experience with web scraping or automated data collection is a plus.
• Familiarity with observability, data quality, and distributed systems.

Responsibilities

• Design, build and maintain scalable data pipelines for large datasets.
• Develop data acquisition solutions to keep training data fresh at scale.
• Operate infrastructure for model deployment, evaluation and serving in production.
• Optimize GPU usage, job scheduling and training efficiency.
• Implement monitoring, logging and alerting across pipelines and ML systems.
• Troubleshoot complex distributed systems and improve MLOps practices.

Skills

Python

SQL

Backend

Data pipelines

ETL/ELT

Docker

Kubernetes

Cloud computing

ML lifecycle

MLOps

Observability

Distributed systems

Education

Bachelor's degree or equivalent

Tools

Docker

Kubernetes

Job description

Speechmatics is seeking an ML Data & Platform Engineer to own the infrastructure powering our speech AI models. You will design data pipelines, implement data acquisition at scale, and operate the platform that trains, evaluates, and serves models in production.

You'll work on observability, scalable architectures, and GPU-optimised training while collaborating with the ML team. We offer flexible working with 2–3 in-office days per week in London and opportunities to grow.