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ML Engineer — speech & voice models

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ElevenLabs

Full-time
Senior
Voice AI
Hybrid
24 Holborn Viaduct, London, England EC1A 2BN, United Kingdom · Hybrid
London, United Kingdom
£100k–£140k + equity

This listing is optimized for AI-native talent: clear skills and tools so candidates know if they fit before they apply—and so you spend less time screening mismatches.

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At a glance

How you'll work, where, and key listing dates.

Employment: Full-time
Workplace: Hybrid
Location: 24 Holborn Viaduct, London, England EC1A 2BN, United Kingdom · Hybrid
Compensation: £100k–£140k + equity
Experience level: Senior
Team: Voice AI
Benefits & perks

About this role

What the team is looking for and how this position fits.

ElevenLabs is hiring a Senior ML Engineer — speech & voice models on the Voice AI team (Hybrid · London, United Kingdom). This is a hands-on role in LLM & generative AI—not a generic “AI enthusiast” post.

You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.

ElevenLabs builds state-of-the-art voice AI for creators, developers, and enterprises. London ML teams work on TTS, voice cloning, and real-time inference.

ML Engineer — speech models: optimise latency and quality for API customers streaming millions of requests daily.

Hybrid role with GPU lab access and tight loop between research prototypes and production serving.

What you will do

Improve neural TTS quality, prosody, and multilingual coverage.
Profile GPU inference paths; reduce p99 latency without quality regression.
Build eval suites for MOS proxies, speaker similarity, and safety filters.
Partner with product on developer-facing API ergonomics.

Stack & environment

Python, PyTorch, CUDA, Docker on AWS.
Hybrid Holborn office + remote focus blocks.

What we look for

Speech, audio ML, or generative sequence modelling in production.
Strong experimentation discipline and ablation documentation.
Portfolio or paper demonstrating audio quality improvements.

Skills we're hiring for

What each capability means for this role—not a keyword checklist.

Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
PyTorch
Training and inference with PyTorch: reproducible experiments, checkpointing, and deployment-minded exports.
NLP

Tools & stack

Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).

AWSTool
Regular use of AWS in the LLM & generative AI stack—configuration, debugging, and team conventions.
DockerTool
Containerized services and reproducible dev environments for ML and LLM workloads.

You're a strong fit if…

Tailored to this listing—use it as a self-check before you apply.

You can show evidence in your application: TTS, voice cloning, or latency optimisation project with metrics.
Several of these show up in recent shipped work—not only on your CV: Python, PyTorch, NLP, AWS, and Docker.
You have owned LLM features, retrieval, agents, and evaluation in production: debugging live issues, running postmortems, and iterating from real user or business signals.
You are comfortable with the hybrid rhythm (Hybrid · London, United Kingdom)—onsite collaboration when it matters, deep work when remote.
You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.
You read the full listing (comp: £100k–£140k + equity, benefits in At a glance) and your expectations align.

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What the employer asked for

TTS, voice cloning, or latency optimisation project with metrics.

Register, add a headline and bio, then you can attach a CV link or PDF on My account before you apply. That keeps applications tied to your profile and makes it easier for employers to review your background.

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