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 . Join EvenUp as a Staff Machine Learning Engineer and build the future of personal injury law technology. This is a unique opportunity to lead the development of high-visibility, high-impact generative AI models that are core to our customer experience and business growth. You will tackle the most complex legal document challenges, turning raw legal and medical data into production-ready models that power Piai™ , our proprietary claims-intelligence platform. You’ll partner closely with Product, Research, and Engineering leaders to shape our modeling strategy, owning critical areas like Document AI, LLM fine-tuning, and sophisticated information retrieval. In this high-priority role, you’ll be empowered to drive innovation, mentor a top-tier ML team, and set the vision for how machine learning will shape both our customer impact and company success. The right leader will see their work translate to tangible product launches, a robust ML foundation, and a direct influence on the success of an ambitious, fast-growing company. What you’ll do: Design and refine advanced Document AI models for entity/relationship extraction, document structure understanding, and sophisticated reasoning from complex legal and medical text. Solve complex modeling challenges involving long-context and multi-document reasoning, including context segmentation and the aggregation of distributed facts. Lead LLM fine-tuning initiatives , applying techniques like reinforcement learning with verifiable reward signals and parameter-efficient fine-tuning (e.g., LoRA, QLoRA). Establish rigorous evaluation standards to reduce hallucinations, improve factual consistency, and handle ambiguous or noisy data. Drive data excellence by conducting hands-on analysis to ensure high-quality training and evaluation datasets, managing edge cases, noise, and data drift. Experiment with and benchmark advanced prompt engineering techniques (few-shot, chain-of-thought), balancing context length with extraction accuracy. Provide technical leadership and mentorship to a team of ML engineers and data scientists, fostering a culture of technical excellence and continuous growth. Collaborate cross-functionally with product, engineering, and legal subject-matter experts to translate ambiguous research goals into impactful production solutions. Act as a bridge between cutting-edge research and practical application, ensuring new techniques are integrated into our production frameworks effectively. What we look for: A true builder’s mentality : ready to launch, scale, and shape a new technical domain within a rapidly growing company. Deep domain expertise in machine learning, NLP, and LLMs, with a track record of solving complex modeling challenges and deploying models in operational settings. Expertise in advanced ML techniques , including deep learning, reinforcement learning, probabilistic modeling, or optimization. Strong record of mentorship , with a passion for guiding team members and establishing best practices for experimentation and benchmarking. Experience and comfort with modern ML engineering languages and frameworks—specifically high proficiency in Python and major LLM technologies. Excellent communication skills , with the ability to partner closely with both technical and non-technical stakeholders to deliver business-impactful solutions. Minimum
