Machine Learning Engineer – TTS

Aiphoria

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

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

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in extending TTS systems through data collection, model adaptation, and architectural improvements, with a strong focus on production readiness and performance optimization. Proficient in deep learning frameworks and audio signal processing, with a solid foundation in software engineering practices.

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

Speech TechnologyText NormalizationModel AdaptationPython ProgrammingC++ ProgrammingData CurationNeural Audio CodecsTransformersTTS Evaluation MethodsSpeech Data Processing

Soft Skills

Independent ExplorationCollaboration

Tools & Technologies

MLflowDVCExperiment Tracking Tools

Industry Keywords

TTS ArchitecturesModel RobustnessLatency OptimizationLow-Resource LanguagesPhonetics

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
Improve model robustness, latency, and quality in collaboration with MLOps on deployment
Advance existing models 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 better and why
Work within a small TTS team of 2–3 ML engineers, supported by MLOps and data engineering teams

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

21 vacation days + public holidays + 5 sick days
Private English lessons via Preply
Fully remote across Europe
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
Fast career progression
Startup pace with enterprise stability — real clients, real revenue, no bureaucracy