Machine Learning Engineer - LLMs & Document AI

Position: Staff Machine Learning Engineer - LLMs & Document AI
Location

San Francisco (hybrid), Toronto (hybrid)

Employment Type

Full time

Location Type

Hybrid

Department

Data Science

Compensation

• San Francisco $215K – $323K
• Offers Equity

• Toronto, Canada CA $180K – CA $271K
• Offers Equity

The salary range provided reflects the compensation that Even Up reasonably expects to offer for this role. The specific salary within this range will be determined based on various factors, including the candidate's relevant experience, education, skills, location, and alignment with the role's responsibilities.

Even Up 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. Even Up is backed by top VCs, including Bessemer Venture Partners, Bain Capital Ventures, Signal Fire, and Lightspeed. We are looking to expand our team with talented, driven, and collaborative individuals who seek to have a lasting impact. Learn .

About the team

At Even Up, 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 Even Up’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…