Associate NLP / Machine Learning ScientistLightcast
Job Summary
Lightcast is seeking an Associate NLP / Machine Learning Scientist to join our AI/ML Lab and help develop NLP models for large-scale labor market data. You will work with senior scientists and engineers to build and improve models that transform unstructured data such as job postings and professional profiles into structured labor market intelligence. Core problem areas include classification, information extraction, entity recognition, normalization, semantic matching, and multilingual NLP.
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.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, NLP, Data Science, or a related field.
- Experience with Python and frameworks such as PyTorch, Hugging Face, or Scikit-learn.
- Familiarity with transformers, embeddings, classification, information extraction, or entity normalization.
- Strong foundation in machine learning, NLP, and deep learning.
- Experience training and evaluating machine learning models.
- Strong analytical and problem-solving skills.
- Ability to work collaboratively and learn quickly in a research and production environment.
Nice to Have
- Internship, academic, or professional experience building NLP or ML models.
- Familiarity with LLMs, 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 ML tooling.
