ML Data & Platform Engineer

Role Overview

What You’ll Do

• Design and maintain scalable pipelines for ingesting, transforming, validating, and storing training datasets.
• Develop web scraping and data acquisition solutions to maintain fresh, high-quality datasets.
• Build and operate infrastructure for model deployment, evaluation, and reliable production serving.
• Optimize GPU utilization, job scheduling, training efficiency, iteration speed, and production reliability.
• Implement monitoring, logging, and alerting across data pipelines and ML systems.
• Troubleshoot distributed-system issues across data infrastructure, training, and inference.
• Improve data and MLOps practices and help shape the platform roadmap.

What You’ll Need

• Strong proficiency in Python and SQL with a backend or data engineering foundation.
• Hands-on experience with Docker, Kubernetes, and a major cloud provider.
• Experience building data pipelines and ETL/ELT processes at scale, including web scraping or automated data collection.
• Understanding of the ML lifecycle from data through model training, evaluation, and serving.
• Experience with data quality practices and/or production-grade observability.
• Ability to design resilient, scalable architectures and troubleshoot distributed systems.
• MLOps experience involving model serving, experiment tracking, training optimization, or reproducible workflows.
• Ability to identify problems and drive fixes without detailed direction.

About Speechmatics

Workplace

• Speechmatics uses a hybrid approach with two to three designated office days each week.
• The arrangement combines remote work with regular in-person collaboration.

Benefits

• Benefits may include private medical and dental coverage, global working opportunities, holiday allowance, pension or 401K matching, and a working-from-home allowance.
• The company describes training and development support across career stages.