Software Engineer, Platform

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

This is a hybrid role, with an expectation of being in our SF office three days per week.

The AI Platform team is responsible for building the foundational systems that make large language models accessible, reliable, and scalable across Even Up’s engineering organization. Sitting at the intersection of backend engineering, ML systems, and platform enablement, this team empowers product and application teams to safely and efficiently leverage LLMs in production. The team has already built and deployed industry-leading legal AI models that extract, structure, and summarize complex documents used by clients every day.

Their work directly powers Even Up’s ability to deliver faster, higher-quality outcomes for injury victims—bringing greater fairness, accessibility, and leverage to an otherwise opaque legal system.

What makes this team unique is its deeply technical, multi-disciplinary collaboration between Data Scientists, ML Researchers, and Software Engineers, all working together to bring AI systems into production  team is focused on building reliable, production-grade AI platforms—not one-off experiments—ensuring that LLM-powered systems are robust, scalable, and safe to operate. By creating shared infrastructure and tooling, the AI Platform team has a high-leverage impact across the entire engineering organization, enabling teams to move faster while maintaining high standards for quality and reliability.

• Own and evolve core backend systems and platforms that enable LLM-powered products at scale

• Drive the technical direction of complex, high-impact initiatives with significant ambiguity

• Partner closely with Product, ML, and Infrastructure teams to translate abstract requirements into robust engineering solutions

• Design and deliver scalable, reliable, and maintainable systems that power production-grade AI applications

• Raise the technical bar through mentorship, best practices, and architectural leadership

• Influence long-term platform and infrastructure roadmaps with a focus on scalability, maintainability, and developer velocity

• Execution & Delivery: Build, ship, and iterate on backend systems that support production-grade LLM applications. Deliver high-quality, maintainable code using a modern tech stack (e.g., Python, Postgres, Kubernetes, Elasticsearch)

• Architecture & Quality: Lead the architecture and design of resilient, scalable distributed systems. Establish and uphold best practices around system design, performance, observability, and reliability

• Cross-Functional Collaboration: Work closely with Product, ML, Data, and Infrastructure teams to scope, design, and deliver impactful solutions. Translate high-level product needs into clear technical plans and execution paths

• Ownership & Growth: Own projects end-to-end—from early design through deployment and continuous iteration. Mentor engineers and act as a force multiplier across the organization. Evaluate and introduce new tools and technologies to continuously improve platform capabilities

• Strong experience building and scaling backend systems, platforms, or infrastructure

• Proven ability to lead complex technical initiatives across teams or domains

• Deep understanding of distributed systems, system design, and technical tradeoff analysis

• High standards for code quality, architecture, and long-term maintainability

• Proficiency in…