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 datageneration to address multilingual and low-resource challenges
Design and implement comprehensive evaluation frameworks to measure modelperformance across precision, recall, F1, and domain-specific benchmarks
Research and implement state-of-the-art techniques from academic papers to improvemodel performance on ASR, NER, intent classification, or entity linking tasks
Optimize models through fine-tuning techniques (LoRA, QLoRA, full fine-tuning) andarchitecture experiments for production deployment
Collaborate with AI engineers to deploy optimized models into production systems andensure reliability in critical healthcare applications
Qualifications
Required
2+ years of experience in ML / DL with focus on training and fine-tuning productionmodels
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 withhands-on experience building these components
Proven experience with model training frameworks (PyTorch, TensorFlow) anddistributed training
Strong understanding of evaluation metrics and ability to design domain-specificbenchmarks
Experience with modern speech models (Whisper, Wav2Vec2, Conformer) or NLPmodels for NER / intent classification (BERT, RoBERTa, BiLSTM-CRF)
Experience with LLM fine-tuning techniques (LoRA, QLoRA, full fine-tuning) orknowledge base integration methods
Proficiency in handling multilingual datasets and cross-lingual transfer learningTrack record of improving model performance through data engineering andaugmentation strategies
Nice to Have
Published research or significant contributions to open-source ML projectsExperience with entity linking to knowledge bases (Wikipedia, DBpedia, domain-specificontologies)
Experience with model optimization techniques (quantization, distillation, pruning)Background in low-resource language modeling or few-shot learning approachesExperience building evaluation frameworks for production ML systemsUnderstanding of information extraction pipelines and knowledge graph construction