Principal Machine Learning Engineer (Up to £135k + Equity) at Speechmatics

Job Title

Principal Machine Learning Engineer

Salary

Up to £135,000 + Equity

Company Description

Speechmatics is a $62M Series B speech‑to‑text intelligence leader based in Cambridge and London, pioneering world‑class Voice AI that supports over 55 languages.

Job Description

As a Principal Machine Learning Engineer, you will co‑define Speechmatics’ technical vision while remaining hands‑on in the Modelling Team. You will bridge the gap between cutting‑edge research and production systems, developing next‑generation transformer models and optimizing inference for global scale. This is a high‑ownership role focused on shipping real‑world impact.

Location

London, UK

Why this role is remarkable

• Lead the technical direction of a $62M Series B scale‑up that recently saw 4x growth in real‑time usage and serves global customers.
• Work at the rare intersection of high‑rigor ML research and large‑scale production, shipping models that solve real‑world problems like accents and noise.
• Take full ownership of technical domains, raising the bar for a talented team of 10+ engineers while developing groundbreaking bilingual and medical models.

What You Will Do

• Develop and deploy advanced ML models using Python and PyTorch, translating research into scalable, maintainable production services.
• Optimise large‑scale model inference using strategies like dynamic batching, flash attention, and speculative decoding to improve speed and cost efficiency.
• Define and enforce best practices for model lifecycle management, data quality, and evaluations across the entire ML stack.

The ideal candidate

• Deep expertise in building and shipping production‑grade ML systems, with a strong foundation in modern transformer architectures and self‑supervised learning.
• Proven track record in distributed training and optimizing inference at scale, bridging the gap between research models and production‑ready code.
• Expert proficiency in Python and ML frameworks such as PyTorch, complemented by experience in MLOps, CI/CD pipelines, and containerisation.

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