Staff Machine Learning Engineer - LLMs & Document AI

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.

About the team:

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.
Our interdisciplinary team - with backgrounds in industry as well as academic research in physics, ML, neuroscience, and more - hopes to foster an environment where we can systematically discover state of the art techniques and be the best in the world at applying them to the challenging problems we encounter in the legal domain. In fact, we already have a number of areas where we exceed the publicly known SOTA and this person will help us expand beyond.

This is a hybrid role with the expectation of working at least 3 days a week from one of our office hubs in San Francisco and Toronto.

What you'll do:

• 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.

What we look for:

• 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.

Job title and level to be determined by several factors including on the candidate's background and experience and interview.

Notice to Candidates:

EvenUp has been made aware of fraudulent job postings and unaffiliated third parties posing as our recruiting team – please know that we have no affiliation or connection to these situations. We only post open roles on our career page (evenuplaw.com/careers) or reputable job boards like our official LinkedIn or Indeed pages, and all official EvenUp recruitment emails will come from the domains @evenuplaw.com, @evenup.ai, @ext-evenuplaw.com, [email protected] or no‑[email protected] email addresses.

To ensure fairness and proper consideration, we do not accept resumes or expressions of interest via email or social media messages. If you’re interested in a role, please submit your application directly through our careers page.

If you receive communication from someone you believe is impersonating EvenUp, please report it to us at [email protected]. Examples of fraudulent domains include “careers-evenuplaw.com” and “careers-evenuplaws.com”.

Benefits & Perks:

As part of our total rewards package, we offer attractive benefits and perks to our employees, including:

• 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

Please note the above benefits & perks are for full-time employees

EvenUp is an equal opportunity employer. We are committed to diversity and inclusion in our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.