Staff Machine Learning Engineer, EvenUp

LLMs & Document AI

$215-323k

Plus equity. Salary applicable to candidates based in San Francisco. Salary for Toronto: CA$180K – CA$271K.

Python

Expert level

San Francisco Bay Area

Toronto

3 days a week in office

AI-driven support for personal injury lawyers

Be an early applicant

AI-driven support for personal injury lawyers

201-500 employees

FintechB2BArtificial IntelligenceLegal

Be an early applicant

$215-323k

Plus equity. Salary applicable to candidates based in San Francisco. Salary for Toronto: CA$180K – CA$271K.

Python

Expert level

San Francisco Bay Area

Toronto

3 days a week in office

201-500 employees

FintechB2BArtificial IntelligenceLegal

Company mission

To close the justice gap using technology and AI.

Job

Company

Role

Who you are

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

What the job involves

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
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
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
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

Our take

Every year, millions of personal injury cases in the US are resolved with only a small fraction going to trial, leaving many victims potentially under-compensated due to inaccurate settlement proposals. EvenUp aims to change this by leveraging AI technology to streamline the assessment of injury cases.

The AI tools evaluate raw case files, including medical and police records, creating tailored "demand packages" that present comprehensive compensation arguments. This improves lawyer performance and victim outcomes.

Despite concerns about AI bias and privacy, EvenUp has garnered significant investor interest, closing a funding round in 2024 that was one of the largest in the history of legal AI. Now valued at over $1 billion, it is investing in its team and introducing new techniques and products for personal injury and other legal areas.

Freddie

Company Specialist at Welcome to the Jungle

Insights

Top investors

Glassdoor (2.8)

Company

Funding (last 2 of 3 rounds)

Oct 2024

$135m

SERIES D

Sep 2024

$35m

SERIES C

Total funding: $220m

Company benefits

Flexible working hours to match your style
Offsites - get to meet your coworkers on a fully-expenses trip ever 6-12 months
A variety of virtual team events such as game nights & happy hours
Choice of medical, dental, and vision insurance plans for you and your family
Flexible paid time off and 10+ holidays per year
A stipend to upgrade your home office for fully-remote roles
401k for US-based employees 🇺🇸

Company HQ

The Castro, San Francisco, CA

Leadership

Rami Karabibar

(Co-founder & CEO)

Graduated from the McGill University with a Bachelor's Degree in Investment Management. Previously worked as a Management Consultant at Bain & Company and as a Startup Generalist at Zoomer.

Raymond Mieszaniec

(Co-founder & COO)

Previously co-founded EquitySim. Also worked as a Risk Consultant at PwC Hong Kong and Mainland China and as an Accountant at Scotiabank.

Saam Mashhad

(Chief of Product & Legal Operations)

Previously worked as a Law Clerk at the Superior Court of Quebec. Also co-founded Compass Startup & Legal Clinic where they served as the Director for 3 years before becoming an Associate at Norton Rose Fulbright.

Salary benchmarks

We don't have enough data yet to provide salary benchmarks for this role.

Submit your salary to help other candidates with crowdsourced salary estimates.

Share this job

View 47 more jobs at EvenUp