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.

ABOUT THE TEAM:

At EvenUp, 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.
We are looking for a curious, impact-driven early career Machine Learning Engineer eager to join EvenUp’s mission. 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.

OPTIONAL SKILLS

• Experience with vector databases (e.g., Pinecone, Weaviate, FAISS, Milvus, Elasticsearch/OpenSearch).
• Familiarity with retrieval frameworks (LangChain, LlamaIndex, custom retrieval pipelines).
• Strong software engineering skills (Python, distributed computing, APIs).
• Strong knowledge of transformer models (LLMs, embeddings, fine-tuning methods like LoRA, PEFT).
• Understanding of evaluation methodologies for generative AI (RAG benchmarks, hallucination reduction, factual grounding).

• Design and implement end-to-end ML systems for retrieval-augmented generation (RAG), vector search, and fine-tuning pipelines.
• Build and optimize data pipelines that integrate structured, unstructured, and embeddings-based data into ML workflows.
• Develop frameworks and reusable components for data extraction, evaluation, and benchmarking of LLMs and other models.
• Collaborate with data scientists and product teams to translate business problems into ML system designs.
• Research, prototype, and productionize state-of-the-art techniques in semantic search, embeddings, and prompt engineering.
• Implement evaluation strategies for ML systems, including relevance metrics, quality scores, and human-in-the-loop workflows.
• Ensure scalability and efficiency of ML workflows, including large-scale embedding generation and retrieval pipelines.
• Work with machine learning platform engineers to integrate ML frameworks into production environments.
• Document system architectures and create internal best practices for building ML/AI frameworks.