Senior ML Engineer (Speech, Audio & NLP)

New

Svitla SystemsSpeech recognition

In EuropeFull-TimeSenior

Salary not disclosed

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Job Details

Strong Python programming skills.
Strong problem-solving and analytical abilities.
Solid knowledge of machine learning and artificial intelligence.
Good understanding of speech processing, audio processing, and NLP concepts.
Hands-on experience with automatic speech recognition or speech-to-text systems.
Experience building or improving audio preprocessing and transcription pipelines.
Experience handling audio formats, sampling rates, noise, segmentation, and audio quality issues.
Experience preparing and managing audio/text datasets for training, evaluation, and analysis.
Experience fine-tuning LLM and/or ASR models for task-specific or domain-specific use cases.
Experience evaluating ASR systems using WER, CER, and task-specific quality metrics, and performing ASR error analysis.
Experience with transcript post-processing, such as text normalization, punctuation restoration, casing, or domain-specific vocabulary correction.
Familiarity with PyTorch or similar deep learning frameworks, and speech/audio and ML tools such as Hugging Face Transformers/Datasets, torchaudio, librosa, pydub, soundfile, Pandas, or NumPy.
Experience with Git/GitHub, Hydra, and Weights & Biases or similar development and experiment tools.
Experience working with shared codebases, remote teams, and remote servers.

Develop, train, evaluate, and deploy machine learning models for speech and audio-related tasks.
Build and optimize end-to-end speech-to-text pipelines, including audio preprocessing, ASR, and transcript post-processing.
Prepare, clean, manage, and manipulate audio and text datasets for model development and evaluation.
Perform audio preprocessing, including resampling, normalization, denoising, segmentation, and format conversion.
Analyze ASR outputs and identify ways to improve transcription quality.
Improve ASR performance through data improvements, model tuning, pipeline optimization, and post-processing.
Apply NLP techniques for transcript cleanup, text normalization, punctuation restoration, casing, and domain-specific correction.
Evaluate speech and NLP systems using WER, CER, precision, recall, and task-specific quality metrics.
Run and track experiments, document findings, and communicate model or pipeline improvements.
Collaborate with team members on shared codebases, remote projects, and remote servers.

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