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 fordomain-specific tasks
Design and implement comprehensive evaluation frameworks to measure modelperformance across critical metrics
Build data pipelines for collecting, annotating, and augmenting training datasetsResearch and implement state-of-the-art techniques from academic papers to improvemodel performance
Collaborate with AI engineers to deploy optimized models into production systemsCreate synthetic data generation pipelines to address data scarcity challengesQualifications
Required
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 learningTrack record of improving model performance through data engineering andaugmentation strategies
Nice to Have
Published research or significant contributions to open-source ML projectsExperience with model optimization techniques (quantization, distillation, pruning)Background in low-resource language modelingExperience building evaluation frameworks for production ML systems