AI role · Open offer
ML Engineer — speech & voice models
ElevenLabs
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.).
- DockerTool
Containerized services and reproducible dev environments for ML and LLM workloads.
- AWSTool
Regular use of AWS in the LLM & generative AI stack—configuration, debugging, and team conventions.
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, Docker, and AWS.
- ✓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.
- ✓
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What the employer asked for
TTS, voice cloning, or latency optimisation project with metrics.
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