Machine Learning Engineer 1 & 2 - LLMs & Document AI , EvenUp
$136-204k
Offers Equity. For candidates based in Toronto, the salary is CA$124K – CA$187K
AI-driven support for personal injury lawyers
Open for applications
AI-driven support for personal injury lawyers
501-1000 employees
Open for applications
$136-204k
Offers Equity. For candidates based in Toronto, the salary is CA$124K – CA$187K
501-1000 employees
Company mission
To close the justice gap using technology and AI.
Role
Who you are
- We are looking for a curious, impact-driven early career Data Scientist / Machine Learning Engineer to join our AI R&D team
- Education: Ph.D., M.S. or B.S. in Computer Science, Machine Learning, Data Science, Statistics, Computational Linguistics, or a closely related field
- Solid grounding in machine-learning fundamentals (supervised & unsupervised learning, evaluation metrics, overfitting/regularization)
- Hands-on experience with NLP or generative-AI techniques (e.g., transformers, embeddings, sequence-to-sequence models, LLMs)
- Proficiency in Python and ML/NLP libraries such as PyTorch, TensorFlow, Hugging Face, spaCy, or similar
- Familiarity with SQL and basic data-engineering concepts (ETL, versioned datasets, notebooks)
- Nice-to-have: exposure to cloud platforms (AWS/GCP), experiment-tracking tools (Weights & Biases, MLflow), or containerized deployment (Docker/Kubernetes)
- Eagerness to learn from senior teammates and iterate quickly in a fast-moving startup
- Clear, concise communication—both written and verbal
- Strong analytical thinking and a bias toward shipping pragmatic, high-impact solutions
What the job involves
- You’ll develop and deploy models that power Piai™, our proprietary claims-intelligence platform, with a focus on machine learning, natural-language processing, and generative AI
- Working alongside senior ML engineers, data scientists, and legal subject-matter experts, you’ll turn raw legal and medical data into production-ready models that directly improve justice for personal-injury clients
- Model research & prototyping – Explore, implement, and benchmark ML/NLP/generative-AI methods (e.g., LLM fine-tuning, retrieval-augmented generation, document understanding)
- Data preparation & feature engineering – Clean, annotate, and transform structured and unstructured case data; build reusable datasets and data loaders
- Experimentation workflow – Design experiments, run A/B tests, analyze results, and communicate findings to the wider product and engineering teams
- Productionization – Help integrate models into our microservices architecture; collaborate with MLOps engineers on packaging, testing, monitoring, and scaling
- Cross-functional collaboration – Pair with product managers, legal analysts, and software engineers to translate pain points into ML solutions and measurable product improvements
- Continuous learning – Stay current with research in LLMs, representation learning, and prompt engineering; share insights through internal talks and docs
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 2025 that was one of the largest in the history of legal AI. Now valued at over $2 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
Company
Funding (last 2 of 4 rounds)
Oct 2025
$150m
SERIES E
Oct 2024
$135m
SERIES D
Total funding: $370m
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
Financial District, 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
(Co-Founder, 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.
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