Staff Machine Learning Engineer, EvenUp
$215-323k
CA$180K – CA$271K + Offers Equity
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
Be an early applicant
$215-323k
CA$180K – CA$271K + Offers Equity
3 days a week in office
201-500 employees
Company mission
To close the justice gap using technology and AI.
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 quantitive field
- 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
- With the field of prompt engineering still evolving, finding the right expertise can be a challenge. 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
- Pioneer cutting-edge Document AI systems at the forefront of generative AI innovation, building next-generation models that go beyond traditional document processing to achieve human-level understanding of complex legal and medical documents, intelligently extract key entities and relationships, perform sophisticated multi-document reasoning, and generate contextually-aware documents that transform business workflows
- Implement and advance technologies in:
- Information Extraction (using traditional ML, LLMs, and multi-modal LLMs for entity recognition, relationship extraction, and document structure understanding)
- Information Retrieval (query understanding, semantic search, hybrid retrieval architectures, and learning-to-rank models)
- Data Management (schema design, knowledge graphs, distributed data pipelines, and petabyte-scale processing)
- RAG (Retrieval-Augmented Generation) with advanced techniques like multi-hop reasoning, chain-of-thought prompting, and self-consistency checks
- Prompt Engineering (few-shot learning, instruction tuning, and context window optimization)
- LLM fine-tuning (parameter-efficient techniques like LoRA/QLoRA, instruction fine-tuning, and domain adaptation)
- Own technical roadmaps and work closely with engineers, product managers, and other stakeholders to integrate research findings into scalable, production-ready solutions
- Help set team best practices on how prompting experimentation and development is done; design and implement efficient procedures for benchmarking and optimizing prompt performance
- Act as a strategic advisor for leaders across the organization on driving business impact through machine learning
- Provide technical leadership and mentorship for a highly skilled team of data scientists and machine learning engineers, guiding them in solving complex business problems
- Collaborate with domain experts (legal, healthcare, etc), product managers, and engineers to translate insights into robust machine learning systems
- Create tools that empower internal teams and clients to make data-driven decisions
- Mentor junior team members, promoting a culture of excellence and collaboration
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
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.
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