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.
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 an experienced Staff 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).

• Lead the design and architecture of large-scale ML systems for retrieval-augmented generation (RAG), vector search, and fine-tuning frameworks across multiple product lines.
• Define and drive technical strategy and best practices for ML system design, including embedding pipelines, evaluation frameworks, and integration with vector databases.
• Mentor and guide other engineers by reviewing designs, code, and system proposals to elevate the technical bar across the ML engineering org.
• Partner with product, research, and infra teams to translate ambiguous business and research goals into robust ML system architectures.
• Drive innovation and prototyping in areas such as semantic search, generative AI evaluation, and fine-tuning techniques, with a focus on production-readiness.
• Own the frameworks and abstractions that make ML workflows reproducible, scalable, and reusable across the company.
• Establish standards for system evaluation, including relevance, latency, cost efficiency, and reliability metrics, and ensure they are consistently applied.
• Act as a bridge between applied ML research and engineering, ensuring that new techniques (LoRA, retrieval optimizations, etc.) are integrated into production frameworks effectively.
• Influence long-term roadmap and platform direction by identifying gaps in ML tooling, infrastructure, and developer experience.
• Represent the ML engineering team in cross-org architectural reviews, ensuring alignment with platform, data, and infra strategies.