About the role
EvenUp is on a mission to close the justice gap using technology and AI. We empower personal injury lawyers and victims to get the justice they deserve. Our products enable law firms to secure faster settlements, higher payouts, and better outcomes for victims injured through no fault of their own in vehicle collisions, accidents, natural disasters, and more. We are one of the fastest-growing vertical SaaS companies in history, and we are just getting started. EvenUp is backed by top VCs, including Bessemer Venture Partners, Bain Capital Ventures, SignalFire, and Lightspeed. We are looking to expand our team with talented, driven, and collaborative individuals who seek to have a lasting impact. Learn more at www.evenuplaw.com . Senior Machine Learning Engineer At EvenUp, we leverage cutting-edge AI to bring fairness and accessibility to the legal system. Tackling the most complex legal document challenges requires expertise in data quality, robust model development, and ongoing innovation. We're looking for a Senior Machine Learning Engineer to help build the models and systems powering Piai™, our proprietary claims-intelligence platform. You'll work across the ML stack - from data pipelines to production model deployment - alongside ML engineers, data scientists, and legal subject-matter experts to turn raw legal and medical data into systems that directly improve outcomes for personal-injury clients.
What You'll Do Design, build, and own production ML systems across the full lifecycle - problem framing, data strategy, training, evaluation, deployment, and monitoring. Architect scalable data pipelines that handle structured, unstructured, and embeddings-based data for training and inference. Build reusable frameworks and infrastructure for model development, evaluation, and benchmarking. Partner with data scientists and product managers to translate ambiguous business problems into concrete ML system designs. Apply and productionize state-of-the-art techniques across NLP, information retrieval, and generative AI where the problem calls for it. Define and implement evaluation strategies - quality metrics, human-in-the-loop review, automated benchmarks - to ensure model reliability. Drive scalability and efficiency across ML workflows, from large-scale data processing to real-time inference. Work with ML platform engineers to integrate models and frameworks into production environments. Document system architectures and establish best practices that other engineers build on. Mentor other engineers and contribute technical judgment to hiring and calibration as
