Lightcast - Associate Applied ML Scientist
Requirements
• 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. • Lightcast is a global leader in labor market insights with our headquarters located in Moscow, ID (US) and other offices in the United Kingdom, Europe, and India. We work with partners across six continents to help drive economic prosperity and mobility by providing the insights needed to build and develop our people, our institutions and companies, and our communities. • Lightcast is proud to be an equal opportunity workplace and does not discriminate on the basis of federally protected statuses. Lightcast has always been, and always will be, committed to diversity of thought and unique perspectives. We seek dynamic professionals from all backgrounds to join our teams, and we encourage our employees to bring their authentic, original, and best selves to work. • We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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.
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