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ML Data & Platform Engineer

Speechmatics· Machine Learning
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Hybrid (Cambridge, England, United Kingdom)Full-timePosted 1mo ago

About the role

Ans role to own the infrastructure powering speech AI models: data pipelines, data acquisition, model training/evaluation/serving platform, and end-to-end ML lifecycle tooling within a hybrid working setup.

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

Qualifications

  • 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

Skills

Required:PythonSQLData pipeline designETL/ELTWeb scraping / data acquisitionContainerisation (Docker)KubernetesCloud (major provider)
Nice to have:MLOps

Benefits

  • Private Medical, and Dental for you and your family
  • global working opportunities
  • generous holiday allowance and pension/401K matching
  • working from home allowance for tech or home office equipment
  • laptop and accessories of your choice
  • parential leave support including adoption assistance and reproductive health services
  • flexible working, regular company lunches, and birthday celebrations
Last updated Sep 10, 2026. Interested? Apply on the company site.

About Speechmatics

Speechmatics offer the most accurate AI speech technology for enterprise - with AI transcription, real-time translation and text-to-speech components. Try our Speech API today!

AI speech technologyAI voiceCase studiesLow-latency speech-to-textMultilingualReal-time transcriptionSpeech APIsSpeech APIs powering Voice AI
Company profileWebsite

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