Job Title

Who You Are You're an ML Research Engineer with 2+ years of experience who bridges the gap between cutting-edge research and production systems. You're passionate about training models that perform exceptionally well not just on benchmarks but in real-world applications. You enjoy diving deep into model architectures, experimenting with training techniques, and building robust evaluation frameworks that ensure model reliability in critical applications. Responsibilities ● Train and fine-tune models for speech recognition (ASR) or NLP tasks including intent classification, Named Entity Recognition (NER), and entity linking to knowledge bases in multilingual healthcare contexts ● Build data pipelines for dataset collection, annotation, augmentation, and synthetic data generation to address multilingual and low-resource challenges ● Design and implement comprehensive evaluation frameworks to measure model performance across precision, recall, F1, and domain-specific benchmarks ● Research and implement state-of-the-art techniques from academic papers to improve model performance on ASR, NER, intent classification, or entity linking tasks ● Optimize models through fine-tuning techniques (LoRA, QLoRA, full fine-tuning) and architecture experiments for production deployment ● Collaborate with AI engineers to deploy optimized models into production systems and ensure reliability in critical healthcare applications Qualifications Required ● 2+ years of experience in ML/DL with focus on training and fine-tuning production models ● Deep expertise in speech recognition systems (ASR) or natural language processing (NLP), including transformer architectures ● Strong understanding of NER, intent classification, or entity linking systems with hands-on experience building these components ● Proven experience with model training frameworks (PyTorch, TensorFlow) and distributed training ● Strong understanding of evaluation metrics and ability to design domain-specific benchmarks ● Experience with modern speech models (Whisper, Wav2Vec2, Conformer) or NLP models for NER/intent classification (BERT, RoBERTa, BiLSTM-CRF) ● Experience with LLM fine-tuning techniques (LoRA, QLoRA, full fine-tuning) or knowledge base integration methods ● Proficiency in handling multilingual datasets and cross-lingual transfer learning ● Track record of improving model performance through data engineering and augmentation strategies Nice to Have ● Published research or significant contributions to open-source ML projects ● Experience with entity linking to knowledge bases (Wikipedia, DBpedia, domain-specific ontologies) ● Experience with model optimization techniques (quantization, distillation, pruning) ● Background in low-resource language modeling or few-shot learning approaches ● Experience building evaluation frameworks for production ML systems ● Understanding of information extraction pipelines and knowledge graph construction