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Clera

AI Infrastructure / Product Engineer

United States$180k–$260kfulltimemidAdded today

About this role

Join a legal AI startup as an AI Infrastructure/Product Engineer to build production systems that transform AI research into reliable tools. You'll own evaluation frameworks, backend services, and features spanning from prototype to deployment, working at the intersection of AI research and product needs.

What you'll do

  • Design and implement evaluation pipelines and benchmarks to measure model accuracy, hallucinations, retrieval quality, latency, and cost
  • Build dashboards and monitoring systems that surface system performance metrics to the team
  • Develop production features for retrieval, drafting, document, and agent workflows from research prototypes
  • Create backend services, APIs, and internal tools to accelerate testing and release cycles
  • Deploy and maintain cloud infrastructure including databases, CI/CD pipelines, and operational reliability
  • Collaborate with AI research teams while maintaining alignment with product and user requirements

What they're looking for

  • Python development
  • Cloud deployment and infrastructure
  • ML/AI evaluation systems and benchmarking
  • Backend services and API design
  • Database design and management
  • CI/CD pipelines and DevOps
  • Monitoring and dashboarding
  • Full-stack development (frontend and backend)

Benefits

  • Base salary $180,000–$260,000 annually
  • Fully remote position
  • Work on cutting-edge AI applications in legal tech
  • Direct influence on product quality and evaluation methodology
  • Collaborative environment spanning research and product teams
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Clera

Clera builds an agentic operating system that automates complex workflows and processes through AI agents, with a platform designed to simplify distributed infrastructure management for developers. The company is hiring Founding Engineers, Customer Engineers, and Product Engineers to develop both backend systems and user-facing interfaces across their AI automation products.

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Likely interview questions

  • Tell us about a time you took an AI or ML research prototype into production—what were the key challenges and how did you address them?
  • Describe your experience building evaluation frameworks or benchmarks for machine learning systems. What metrics mattered most?
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