The RoleWe are looking for someone who likes to solve hard problems. Our opportunity is ideal for someone who thinks critically, is constantly driven by curiosity, enjoys being challenged and creating state of the art solutions and is a builder at heart. We are seeking a skilled and driven Staff Machine Learning Engineer with 12+ years of industry experience to join our team.
You'll play a key role in building and deploying machine learning models and AI systems that are reliable, scalable, and impactful. The ideal candidate has experience applying NLP techniques in production environments and thrives in a fast-paced, collaborative setting.ResponsibilitiesDesign and engineer NLP solutions to solve real-world problems within the tax industry and push state of the art LLMs and assistants.Independently design and implement algorithms, train state of the art large language models (LLM) on large data, and evaluate their performance.Drive engineering and science that can be applied to Black Ore platform developmentFine tune modelsBasic QualificationsMasters degree in Computer Science, Computer Engineering, Artificial Intelligence or relevant technical field, or equivalent practical experience. PhD preferred but not required.Applied Research experience in one or more of these areas: NLP, NLU, machine learning, deep learning, or related fields.Direct experience in Summarization, Classification and/or ExtractionExperience with NER (named-entity recognition)End to end experience delivering production-ready code3+ years of experience with development and implementation of LLM algorithm/systems and model training.Direct experience in generative AI and LLM's, and implementing solutions to productionExperience working with machine learning libraries like Pytorch.Familiar with scripting languages such as Python and shell scripts.Direct experience in Prompt EngineeringContinued interest in LLM trends and the latest in cutting edge models within AIProficient in Python and ML libraries such as TensorFlow, PyTorch, scikit-learn, or similarStrong understanding of ML fundamentals, including supervised/unsupervised learning, model evaluation, feature engineering, and overfitting/underfittingExperience working with large datasets and building production-ready data pipelines