Staff Machine Learning Engineer - LLMs & Document AI

EvenUp • San Francisco, CA

119d

About The Position

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. 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. That's why we're seeking a Staff Machine Learning Engineer eager to join EvenUp's mission.

Requirements

• 10+ years of experience in machine learning with multiple models deployed in operational settings.
• PhD in Machine Learning, Computer Science, or other quantitative fields.
• Strong proficiency with the latest Large Language Model (LLM) technologies.
• Expertise in one or more areas of machine learning, such as deep learning, reinforcement learning, probabilistic modeling, or optimization.
• Strong communication, collaboration, and coaching skills.
• High proficiency in a procedural programming language (e.g. Python).
• Ability to translate and apply cutting edge research into practical solutions.
• Strong leadership and mentorship abilities, with a passion for guiding and developing other team members.

Responsibilities

• Develop Advanced Document AI Models
• Design and refine ML models for entity/relationship extraction, document structure understanding, and sophisticated information retrieval and reasoning from legal and medical text.
• Conduct hands-on data analysis to ensure high-quality training and evaluation datasets, including identification and management of outliers, mislabeled data, edge cases, noise, and drift; work with data stakeholders to iteratively improve data quality.
• Solve Complex Modeling Challenges
• Tackle long-context and multi-document reasoning challenges, including prompt design, context segmentation, and aggregation of distributed facts.
• Develop strategies to reduce hallucinations, improve factual consistency, and handle ambiguous, noisy, or incomplete data.
• Lead LLM Fine-tuning
• Apply reinforcement learning with verifiable reward signals to fine-tune LLMs for factual and extraction accuracy.
• Apply parameter-efficient fine-tuning (e.g., LoRA, QLoRA) to maximize model performance.
• Experiment with and benchmark advanced prompt engineering techniques (few-shot, chain-of-thought, instruction tuning), balancing context length and extraction accuracy.
• Provide Leadership & Collaboration
• Mentor and guide a team of ML engineers and data scientists, fostering a rigorous and creative modeling culture.
• Collaborate with product, engineering, and legal experts to deliver robust, business-impactful solutions.
• Establish and maintain best practices for experimentation, benchmarking, and documentation in modeling.

Benefits

• Choice of medical, dental, and vision insurance plans for you and your family
• Additional insurance coverage options for life, accident, or critical illness
• Flexible paid time off, sick leave, short-term and long-term disability
• 10 US observed holidays, and Canadian statutory holidays by province
• A home office stipend
• 401(k) for US-based employees and RRSP for Canada-based employees
• Paid parental leave
• A local in-person meet-up program
• Hubs in San Francisco and Toronto

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree
• Number of Employees: 251-500 employees