Top Natural Language Processing (NLP) Jobs in Chicago, IL

The role involves developing and improving TTS/Voice Generation models, training, fine-tuning, evaluating speech models, and collaborating across research, engineering, and product teams in a fast-paced startup environment.

Develop and deploy advanced ML solutions for OCR and NLP, optimizing models for performance and integrating them into production workflows while collaborating with cross-functional teams.

Develop AI agents leveraging large language models, integrate external tools, and ensure production-ready AI features while collaborating across teams.

The role involves developing and optimizing machine learning systems for voice experiences, focusing on speech recognition and language understanding. Responsibilities include training models, ensuring performance metrics, and collaborating with cross-functional teams for successful deployment.

Design, fine-tune, evaluate, and deploy LLM-driven, user-facing browser features using RAG, embeddings, prompt engineering, and modern ML tooling. Lead end-to-end model lifecycle, run production experiments, collaborate with product and engineering, and document and review work with an emphasis on privacy, latency, and usability.

As a Staff NLP Scientist, you will solve complex medical document understanding problems using NLP and Computer Vision, optimizing models for performance and scalability.

Develop, evaluate, and deploy AI systems and machine learning models, focusing on NLP and multimodal models. Collaborate with teams and publish research.

Lead ML/NLP work across recommender systems, taxonomies, and agentic AI solutions. Prototype models, advise on data science strategy, mentor teammates, and deliver production-ready, well-documented ML pipelines and insights.

Design, build, and evaluate AI systems, focusing on natural language understanding and automation. Collaborate with teams to ensure production quality and performance metrics.

Remote entry-level NLP engineering role focused on building language processing pipelines. Responsibilities include fetching unstructured text/audio data, preparing data with NLTK/spaCy/Gensim/Librosa, and developing complex deep learning models (CNNs, RNNs, MLPs, autoencoders) in TensorFlow or PyTorch for classification, regression, clustering, anomaly detection and structured outputs.