Machine Learning Research Engineer

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 challenges

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

● 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