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Clera

ML Infrastructure Engineer

San MateofulltimemidAdded today

About this role

Own the end-to-end inference and model-serving infrastructure for AI agents at an early-stage enterprise AI company. Design scalable, reliable systems that support production workloads in regulated industries while optimizing performance under growing concurrent load.

What you'll do

  • Design and build inference and model-serving infrastructure from architecture through production deployment
  • Scale systems to handle increasing concurrent load while maintaining reliability and efficiency
  • Identify and resolve infrastructure bottlenecks in collaboration with ML and platform teams
  • Drive performance optimization across latency, throughput, and reliability metrics
  • Manage containerization and orchestration of ML workloads using Docker and Kubernetes
  • Implement monitoring and observability solutions for production systems

What they're looking for

  • ML inference systems and model-serving platforms (TensorFlow Serving, TorchServe, Triton, KServe)
  • Distributed systems design and concurrent request management
  • Docker and Kubernetes orchestration
  • Cloud platforms (AWS, GCP, or Azure)
  • Monitoring and observability tools (Prometheus, Grafana, ELK, distributed tracing)
  • Systems or backend programming (Python, Go, Rust, C++, or Java)
  • Knowledge graphs and semantic search
  • Real-time inference and agentic AI systems
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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

  • Walk us through a production ML inference system you've designed and scaled. What were the primary bottlenecks and how did you address them?
  • How do you approach monitoring and observability for model-serving infrastructure, and what metrics matter most to you?
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