Senior ML Engineer (Speech, Audio & NLP)
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Svitla Systems Inc.Speech and audio AI
Workplace type: remote; Locations: Gdańsk, Warszawa, Kraków, Łódź, Wrocław, Poznań, Katowice, Białystok, Szczecin, Zielona Góra; Country code: PL; full-time position in EuropeFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- En B2
- Required Skills
- PythonArtificial IntelligenceMachine LearningPyTorchNLPLLM
Requirements
- Strong Python programming skills.
- Solid knowledge of machine learning and artificial intelligence, with a good understanding of speech processing, audio processing, and NLP.
- 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 with WER, CER, and task-specific quality metrics, and analyzing ASR errors.
- 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.
- Familiarity with Hugging Face Transformers/Datasets, torchaudio, librosa, pydub, soundfile, Pandas, NumPy, or similar tools.
- Experience with Git/GitHub, Hydra, and Weights & Biases or similar development and experiment tools.
- Experience working with shared codebases, remote teams, and remote servers.
Responsibilities
- 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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