Machine Learning Research Engineer

3 days ago


New Delhi, India Recro Full time

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 and natural language processing in multilingual healthcare contexts ● Develop specialized models through fine-tuning and optimization techniques for domain-specific tasks ● Design and implement comprehensive evaluation frameworks to measure model performance across critical metrics ● Build data pipelines for collecting, annotating, and augmenting training datasets ● Research and implement state-of-the-art techniques from academic papers to improve model performance ● Collaborate with AI engineers to deploy optimized models into production systems ● Create synthetic data generation pipelines to address data scarcity challengesQualifications 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 ● 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 LLM fine-tuning techniques (LoRA, QLoRA, full fine-tuning) ● 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 model optimization techniques (quantization, distillation, pruning) ● Background in low-resource language modeling ● Experience building evaluation frameworks for production ML systems



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