Associate Applied ML Scientist - 1087
Confirmed on the employer's own hiring board on Oct 26, 2026. First seen by Alion on Oct 7, 2026. Lightcast scores A on the Alion truth index.
Lightcast is seeking an Associate NLP / Machine Learning Scientist to join our AI/ML Laband help develop NLP models for large-scale labor market data.You will work with senior scientists and engineers to build and improve models that transformunstructured data such as job postings and professional profiles into structured labor marketintelligence. Core problem areas include classification, information extraction, entity recognition, normalization, semantic matching, and multilingual NLP.
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.