Machine Learning Engineer - Systems

Even Up 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. Even Up is backed by top VCs, including Bessemer Venture Partners, Bain Capital Ventures, Signal Fire, 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

Join Even Up as a Staff Machine Learning Engineer and build the future of personal injury law technology. This is a unique opportunity to lead the design and architecture of high-visibility, high-impact generative AI products that are core to our customer experience and business growth. You’ll tackle the most complex legal document challenges by developing and deploying models that power Piai™, our proprietary claims-intelligence platform.

You’ll partner closely with Product, Research, and Infrastructure leaders to shape our technical strategy and roadmap, owning critical areas like Retrieval-Augmented Generation (RAG), vector search, and fine-tuning frameworks. In this high-priority role, you’ll be empowered to drive innovation, mentor a top-tier ML team, and set the vision for how machine learning will shape both our customer impact and company success. The right leader will see their work translate to tangible product launches, a robust ML foundation, and a direct influence on the success of an ambitious, fast-growing company.

• Lead the design and architecture of large-scale ML systems for 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 and evaluation frameworks.

• Provide hands-on technical leadership by reviewing designs, code, and system proposals to elevate the technical bar across the ML engineering org.

• Collaborate cross-functionally with Product and Engineering to translate ambiguous business goals into robust, impactful ML architectures.

• Drive innovation and prototyping in semantic search and generative AI evaluation, ensuring new techniques like LoRA are effectively integrated into production.

• Own the frameworks and abstractions that make ML workflows reproducible, scalable, and reusable across the company.

• Establish standards for system evaluation
  , focusing on relevance, latency, cost efficiency, and reliability metrics.

• Influence the long-term roadmap by identifying gaps in ML tooling, infrastructure, and developer experience.

• Mentor and guide other engineers
  , fostering a culture of technical excellence and continuous growth.

• A true builder’s mentality
  : ready to launch, scale, and shape a new technical domain within a rapidly growing company.

• Deep domain expertise in machine learning, NLP, LLMs, and generative AI, with hands-on experience taking complex systems to production.

• Strong record of mentorship
  , with a passion for guiding team members and representing the ML team in cross-org architectural reviews.

• Experience and comfort with modern ML engineering languages and frameworks—Python, PyTorch, and vector databases (e.g., Pinecone, Milvus, or FAISS).

• Excellent communication skills
  , with the ability to bridge the gap between applied ML research and production engineering.

• 5+ years of hands‑on professional experience in machine learning engineering or related fields, with multiple models deployed in operational settings.

• Deep hands‑on expertise in transformer models, embeddings, and fine‑tuning methods (e.g., LoRA, PEFT).

• Strong proficiency in Python and major ML/NLP frameworks such as PyTorch, Tensor Flow, or Hugging Face.

• Demonstrated ability to lead technical strategy
  , develop roadmaps, and drive execution in fast‑paced, ambiguous environments.

• Experience working in a high‑growth startup…