We are looking for a Machine Learning Engineer with a focus on Natural Language Processing (NLP) and Automatic Speech Recognition (ASR).
This role is ideal for someone with solid ML fundamentals who has hands-on experience with real-world data and is interested in working with text and speech-based systems.
Requirements:
— 2–4+ years of experience. — Strong understanding of how models perform and behave in production environments. — Experience with GPU-based training is a strong plus. — Python: Strong proficiency. — Data Science Stack: Hands-on experience with NumPy, Pandas, and scikit-learn. — DL Frameworks: Experience with PyTorch (preferred) or TensorFlow. — English level: Upper-Intermediate or higher.
ML & Mathematics: — Solid understanding of linear algebra, probability, and statistics. — Practical knowledge of classic algorithms (regression, classification, clustering). — Deep understanding of performance metrics (Accuracy, Precision, Recall, F1) and the Bias-Variance tradeoff.
NLP: Experience with: — Tokenization, lemmatization — TF-IDF, Bag of Words, n-grams Understanding of Transformers architecture Experience with: — Hugging Face Transformers Practical experience with: — Text classification — Named Entity Recognition (NER) — Fine-tuning pre-trained models.
ASR (Automatic Speech Recognition): Understanding of: — Spectrograms — MFCC Experience with audio processing: — librosa, torchaudio Familiarity with: — CTC (Connectionist Temporal Classification) — Seq2Seq + Attention — Wav2Vec 2.0 — WhisperX Practical experience with: — Speech-to-text pipelines; — Audio preprocessing and cleaning; — Data augmentation (noise, speed, etc.) Familiarity with metrics: — WER (Word Error Rate) — PER (Phoneme Error Rate).
Nice to have: — Production deployment. — Working with noisy data.
