Machine Learning TTS Engineer

Aiphoria

. Extend TTS to new languages through data collection and curation, text normalization/G2P, and model adaptation .

Posted 11/4/2026full-timeRemote • PortugalMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in extending TTS systems to new languages through data collection, model adaptation, and architectural improvements. Proficient in deep learning frameworks and audio signal processing, with a strong focus on making TTS models production-ready and efficient.

Highest-signal resume keywords

TTS Model Production-ReadinessDeep Learning Frameworks (PyTorch)Modern LLM-Based TTS ApproachesAudio Signal ProcessingMLOps/DataOps Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

TTS ExperienceDeep LearningPython ProgrammingC++ ProgrammingText NormalizationData CurationModel AdaptationSpeech Data ProcessingBenchmark DesignTTS Evaluation Methods

Soft Skills

Independent ExplorationProblem Solving

Tools & Technologies

MLflowDVCTransformersNeural Audio CodecsVocoder

Certifications & Qualifications

BS in Computer Science or Related Field

Industry Keywords

Speech TechnologyTTS ArchitecturesLow-Resource LanguagesDataset CreationPublications in Speech/ML Venues

Tech Stack

Tools & technologies

PythonPyTorchC++

About the role

Key responsibilities & impact

Extend TTS to new languages through data collection and curation, text normalization/G2P, and model adaptation
Take new TTS architectures from research to production by improving 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 to determine whether new model iterations are genuinely better and why
Join a small TTS team of 2–3 ML engineers supported by a dedicated MLOps team and a data engineer

Requirements

What you’ll need

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+
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

Benefits

Comp & perks

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