AI role · Open offer

Senior Voice AI Engineer — speech & TTS

ElevenLabs

Full-time
Senior
Speech research
Hybrid
New York, NY, United States · Hybrid
New York, United States
USD 165k–220k + 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.

4 skills2 tools

At a glance

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

Employment: Full-time
Workplace: Hybrid
Location: New York, NY, United States · Hybrid
Compensation: USD 165k–220k + equity
Experience level: Senior
Team: Speech research
Benefits & perks

About this role

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

ElevenLabs is hiring a Senior Senior Voice AI Engineer — speech & TTS on the Speech research team (Hybrid · New York, United States). 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 leads generative voice and conversational speech AI.

Senior Voice AI Engineer (hybrid NYC): TTS, ASR, and real-time voice pipelines with quality and latency targets.

Missions

Improve speech models and streaming inference for natural, low-latency voice.
Ship evals for MOS, WER, and production regressions.

Stack & environment

Python, PyTorch, CUDA, Docker.

What we look for

Speech / audio ML in production; research-to-prod mindset.

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.
CUDATool
Regular use of CUDA 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, ASR, or real-time voice pipeline with latency/quality metrics.
Several of these show up in recent shipped work—not only on your CV: Python, PyTorch, NLP, Machine Learning, 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 · New York, United States)—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.

Apply

Candidate accounts only: add your headline and bio on , and optionally a CV link or PDF so recruiters see your full résumé. Then write a short pitch below—applications use your login email.

What the employer asked for

TTS, ASR, or real-time voice pipeline with latency/quality 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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