Machine Learning Engineer

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.

We're looking for a Machine Learning Engineer to join our ML team at EvenUp. 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 outcomes for personal-injury clients.

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  Explore, implement, and benchmark ML, NLP, and generative AI methods - including LLM fine-tuning, retrieval-augmented generation, and document understanding.

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  Clean, annotate, and transform structured and unstructured case data, and build reusable datasets and data loaders.

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  Design experiments, run A/B tests, analyze results, and communicate findings to product and engineering teams.

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  Help integrate models into our production systems, partnering with ML platform engineers on testing, monitoring, and scaling.

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  Pair with product managers, legal analysts, and software engineers to translate real problems into ML solutions and measurable improvements.

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  Stay current with research in LLMs, representation learning, and prompt engineering, and share what you learn with the team.

• B.S., M.S., or Ph.D. in Computer Science, Machine Learning, Data Science, Statistics, Computational Linguistics, or a related field.

• Solid grounding in ML fundamentals - supervised and unsupervised learning, evaluation metrics, overfitting and regularization.

• Hands-on experience with NLP or generative AI techniques - transformers, embeddings, sequence-to-sequence models, or LLMs.

• Proficiency in Python and ML/NLP libraries such as PyTorch, TensorFlow, Hugging Face, or spaCy.

• Familiarity with SQL and basic data engineering concepts - ETL, versioned datasets, notebooks.

• Strong analytical thinking, clear communication, and eagerness to learn and iterate quickly in a fast-moving startup.

• Ability to work hybrid (3 days your choice) in our San Francisco or Toronto, Canada office.

• Exposure to cloud platforms (AWS, GCP).

• Experience with experiment-tracking tools (Weights & Biases, Gemini Enterprise Agent Platform, MLflow).

• Familiarity with containerized deployment (Docker, Kubernetes).

• Experience in a high-growth startup environment.