Senior Machine Learning Engineer - NLP/LLM

Machine Learning Engineer In this role you build and deploy production ML and LLM systems that extract insights from complex legal documents. You will tackle demanding NLP and document intelligence problems, collaborating with ML engineers, legal experts, product teams, and security partners. You translate business needs into scalable ML solutions and contribute to a product-driven, privacy-conscious environment. The opportunity combines cutting-edge tech with real-world impact in the legal domain. Compensation /

Hybrid Work Model Flexible vacation Mental Health Days Tuition reimbursement Employee Stock Purchase Plan 401k plan with company match

Design, train, and deploy ML and LLM-based models for NLP and document intelligence Create production solutions for information extraction, text generation and summarization, AI agents, search, and document analysis Build scalable ML pipelines supporting training, evaluation, deployment, and production use Develop model evaluation frameworks to assess quality, reliability, drift, and bias Optimize performance and resource use via experiments, feature engineering, and tuning Translate business problems into practical ML solutions and push them from experimentation to production at scale Collaborate across ML, engineering, product, legal, data, and security teams to deliver trusted AI capabilities while protecting sensitive data Key

3+ years of professional ML engineering, applied ML, or related software engineering experience Hands-on experience building, training, and deploying models into production with clear personal contribution Strong practical experience with ML, NLP, and modern LLM architectures Experience with information extraction, text generation/summarization, AI agents, or search Advanced Python skills and experience with PyTorch or TensorFlow Experience fine-tuning language models or domain-specific ML models Ability to design and apply evaluation methods to production ML systems Ability to translate product or business problems into ML solutions and communicate technical decisions strong problem-solving collaboration ownership Natural language processing LLM architectures information extraction