Associate Applied ML Scientist

Major Responsibilities:

Develop and improve NLP models for classification, extraction, normalization, entity linking, and semantic matching.

Train and evaluate transformer-based and embedding-based models using architectures such as BERT, DeBERTa, ModernBERT, and Sentence Transformers.

Support experimentation with LLMs and foundation models, including prompt-based approaches and fine-tuning techniques.

Perform model evaluation, error analysis, and data analysis to identify opportunities for improvement.

Help optimize models for production with a focus on accuracy, latency, throughput, and cost.

Build and maintain reproducible training and evaluation pipelines.

Work with multilingual labor market data and support cross-lingual model development.

Collaborate with senior scientists, ML engineers, and data engineers to productionize models.

Stay current with advances in NLP, LLMs, embeddings, and machine learning.

Education and Experience:

• Bachelor’s or master’s degree in computer science, machine learning, NLP, data science, or a related field.

• Strong foundation in machine learning, natural language processing, and deep learning.

• Experience using Python and ML frameworks such as PyTorch, Hugging Face, or scikit-learn.

• Familiarity with transformers, embeddings, classification, information extraction, or entity normalization.

• Experience training and evaluating machine learning models, with strong analytical and problem-solving skills.

• Ability to collaborate and learn quickly in research and production environments.

• Internship, academic, or professional experience building NLP or machine learning models.

• Familiarity with large language models, fine-tuning, LoRA, PEFT, or prompt engineering.

• Exposure to multilingual NLP, semantic search, retrieval, or entity linking.

• Familiarity with model optimization techniques such as quantization or distillation.

• Experience with AWS, SageMaker, Docker, MLflow, or similar machine learning tools.