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Apply in 3 Minutes : Machine Learning Engineer

RecroBengaluru, Karnataka, India

30+ days ago

Job description

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

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Machine Learning Engineer • Bengaluru, Karnataka, India