Machine Learning Research Engineer

4 weeks ago

Bengaluru Karnataka India, Karnataka Recro Full time

Who You AreYou're an ML Research Engineer with 2+ years of experience who bridges the gap betweencutting-edge research and production systems. You're passionate about training models thatperform exceptionally well not just on benchmarks but in real-world applications. You enjoydiving deep into model architectures, experimenting with training techniques, and buildingrobust evaluation frameworks that ensure model reliability in critical applications.Responsibilities● Train and fine-tune models for speech recognition and natural language processing inmultilingual healthcare contexts● Develop specialized models through fine-tuning and optimization techniques fordomain-specific tasks● Design and implement comprehensive evaluation frameworks to measure modelperformance 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 improvemodel performance● Collaborate with AI engineers to deploy optimized models into production systems● Create synthetic data generation pipelines to address data scarcity challengesQualificationsRequired● 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● 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 LLMfine-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 andaugmentation strategiesNice 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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