[Hiring] Machine Learning TTS Engineer @Acclaim

Role Description

We’re expanding our TTS to new languages and moving to next-generation LLM-based TTS, and we’re looking for a Machine Learning TTS Engineer to help bring these technologies to production quality! You’ll join a small TTS team of 2–3 ML engineers, supported by a dedicated MLOps team and a data engineer.

Qualifications

• 3+ years of experience in speech technology with hands-on TTS experience.
• Hands-on experience with modern LLM-based TTS approaches (e.g., VALL-E, CosyVoice, XTTS).
• Experience making TTS models production-ready: stability, robustness, and inference efficiency at the model level.
• Proficiency in deep learning frameworks, especially PyTorch.
• Understanding of modern ML fundamentals: Transformers, diffusion/flow matching, neural audio codecs, and vocoders.
• Understanding of streaming TTS constraints (latency, chunked/incremental generation) and how they shape model architecture choices.
• Knowledge of audio signal processing and the linguistic side of TTS (phonetics, text normalization).
• Basic experience with speech data processing (segmentation, alignment, quality filtering).
• Familiarity with TTS evaluation methods (MOS, SBS).
• Strong Python skills and solid software engineering practices.
• Proven ability to independently explore new ideas and improve existing ML systems.
• BS or higher in Computer Science or a related field, or equivalent experience.
• English B2+.

Requirements

• Experience with low-resource languages, including dataset creation and augmentation.
• Experience designing recording scripts and specs for TTS voice datasets.
• MLOps/DataOps experience, e.g., MLflow, DVC, or similar experiment tracking tools.
• C++ skills.
• Publications in speech/ML venues.

Responsibilities

• Extend our TTS to new languages: data collection and curation, text normalization/G2P, and model adaptation.
• Take new TTS architectures from research to production: improve robustness, latency, and quality, working closely with MLOps on deployment.
• Push existing models further through post-training, data curation, architectural improvements, and new training approaches.
• Build efficient audio data pipelines, from speaker recording and preprocessing to model training.
• Design rigorous benchmarks and evaluations that show whether a new model iteration is genuinely better, and why.

Benefits

• The team has built award-winning AI products for tech corporations — devices, voice assistants, products that are actually in the world.
• Cutting-edge tech stack: Speech Technologies, NLP, Generative AI (LLMs, diffusion models), voice-first agentic architecture with privacy-first and on-premises deployment.
• High engineering bar and real ownership — the team cares about what actually works in production, not what looks good in a demo, and you'll see the impact of your work directly.
• Fast career progression — a senior-heavy team and a high volume of real problems means you grow faster than you would anywhere else.
• Startup pace with enterprise stability — real clients, real revenue, no bureaucracy.
• Fully remote across Europe.
• 21 vacation days + public holidays + 5 sick days.
• Private English lessons via Preply.