Job description

Important information before applying: Location: This position is based on-site in Moscow, Idaho, and requires five days per week in the office. We will consider only candidates who currently live locally or are willing to relocate. Work Authorization: To respect your time, we cannot provide employment visa sponsorship for this position now or in the future. Before applying, please confirm that you are authorized to work in the United States without current or future employer sponsorship. Lightcast is seeking a Senior NLP / Machine Learning Scientist to join our AI/ML Lab andbuild state-of-the-art NLP models for large-scale labor market data.You will develop and productionize models that transform unstructured data such as jobpostings and professional profiles into structured labor market intelligence. Core problem areasinclude classification, information extraction, entity recognition, normalization, semantic matching, and multilingual NLP .This role spans the full model lifecycle—from experimentation and fine-tuning to evaluation,optimization, and production inference at scale. Major Responsibilities: Build NLP models for classification, extraction, normalization, entity linking, and semantic matching. Develop transformer-based and embedding-based models using architectures such as BERT, DeBERTa, ModernBERT, Sentence Transformers, and modern foundation models. Fine-tune and adapt LLMs using techniques such as supervised fine-tuning, LoRA, QLoRA, PEFT, and distillation. Evaluate when to use LLMs versus smaller task-specific models based on accuracy, latency, throughput, and cost. Optimize models for production using quantization, distillation, batching, model compilation, and efficient inference frameworks. Develop multilingual NLP models and cross-lingual approaches for global labor market data. Design robust evaluation frameworks, perform error analysis, and continuously improve model quality. Partner with ML and data engineers to deploy scalable models and training pipelines in AWS. Stay current with advances in NLP, LLMs, embeddings, and efficient machine learning, and translate promising research into production systems. Mentor team members and contribute to technical standards and modeling strategy. Education and Experience: Master's degree or PhD in Computer Science, Machine Learning, NLP, or a related field. Strong experience building and deploying production NLP or ML systems. Deep understanding of transformers, embeddings, classification, information extraction, and entity normalization. Strong Python and PyTorch experience; hands-on experience with Hugging Face or similar frameworks. Experience with large-scale model training, evaluation, and productionization. Strong problem-solving skills and ability to independently drive ambiguous ML problems from research to production. Hands-on experience fine-tuning LLMs or foundation models. Experience with LoRA, QLoRA, PEFT, instruction tuning, or model distillation. Experience with model inference optimization, including quantization, ONNX, TensorRT, vLLM, TorchCompile, or GPU optimization. Experience with multilingual NLP, semantic search, retrieval, entity linking, or knowledge graphs. Experience with AWS, SageMaker, Docker, MLflow, or similar MLOps tooling. 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.

Requirements

Must-haves

Location: This position is based on-site in Moscow, Idaho, and requires five days per week in the office. We will consider only candidates who currently live locally or are willing to relocate.
Build NLP models for classification, extraction, normalization, entity linking, and semantic matching.
Develop transformer-based and embedding-based models using architectures such as BERT, DeBERTa, ModernBERT, Sentence Transformers, and modern foundation models.
Fine-tune and adapt LLMs using techniques such as supervised fine-tuning, LoRA, QLoRA, PEFT, and distillation.
Evaluate when to use LLMs versus smaller task-specific models based on accuracy, latency, throughput, and cost.
Optimize models for production using quantization, distillation, batching, model compilation, and efficient inference frameworks.
Develop multilingual NLP models and cross-lingual approaches for global labor market data.
Design robust evaluation frameworks, perform error analysis, and continuously improve model quality.
Partner with ML and data engineers to deploy scalable models and training pipelines in AWS.
Stay current with advances in NLP, LLMs, embeddings, and efficient machine learning, and translate promising research into production systems.
Mentor team members and contribute to technical standards and modeling strategy.
Master's degree or PhD in Computer Science, Machine Learning, NLP, or a related field.
Strong experience building and deploying production NLP or ML systems.
Deep understanding of transformers, embeddings, classification, information extraction, and entity normalization.
Strong Python and PyTorch experience; hands-on experience with Hugging Face or similar frameworks.
Experience with large-scale model training, evaluation, and productionization.
Strong problem-solving skills and ability to independently drive ambiguous ML problems from research to production.
Hands-on experience fine-tuning LLMs or foundation models.
Experience with LoRA, QLoRA, PEFT, instruction tuning, or model distillation.
Experience with model inference optimization, including quantization, ONNX, TensorRT, vLLM, TorchCompile, or GPU optimization.
Experience with multilingual NLP, semantic search, retrieval, entity linking, or knowledge graphs.
Experience with AWS, SageMaker, Docker, MLflow, or similar MLOps tooling.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaimlawspythondocker

Platform: lever
Effort: quick