Principal Machine Learning Engineer

About UsBlack Ore is building the leading AI platform for financial services. By combining LLMs, proprietary AI/ML and automation we accelerate core workflows for the industry, allow financial services professionals to be more productive and enable consumers to enhance their personal finance. Our flagship product, Tax Autopilot, combines AI with federal and state tax codes & regulations to simplify the tax preparation and review process for Certified Public Accountants (CPAs) and accounting firms.Founded in 2022, we launched with $60 million in early stage funding from some of the world’s leading investors including a16z, Founders Fund, General Catalyst, Khosla Ventures, Oak HC/FT, Trust Ventures and leading tech founders/angel investors including Jason Gardner (Founder and CEO of Marqeta), Max Levchin (Founder of Paypal and Affirm), Tom Glocer (Former CEO of Thomson Reuters), Gokul Rajaram, and Mark Britto (EVP, CPO, PayPal).Our team has an incredibly ambitious vision to completely transform the way businesses and consumers interact in financial services. We’re looking to hire strong team members to grow the team. Some of the traits we look for are:Owner Mentality – Desire to take initiative, identify problems and implement solutionsMission Driven – Passion for building AI/ML solutions that reimagine how businesses and consumers operateIntellectually Curious – Excitement going deep and building detailed understanding of the function, role, customer and problem spaceTeam Oriented – Ability to collaborate respectfully and put the team above the selfThe RoleWe are looking for someone who likes to solve hard problems. Our opportunity is ideal for someone who thinks critically, is constantly driven by curiousity, enjoys being challenged and creating state of the art solutions and is a builder at heart.We are seeking a skilled and driven Principal 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 dependently 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 pipelinesWhat We OfferCompetitive salary and equity based compensationEmployer-paid medical, dental and vision insuranceAbility to define your own successContinuous learning and new challenges to master #J-18808-Ljbffr

Created: 2025-09-25