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