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Clera

ML Infrastructure Engineer

San MateofulltimemidAdded 2 days ago

About this role

Early-stage enterprise AI company seeks an ML Infrastructure Engineer to design, build, and operate inference and model-serving infrastructure for AI agents in regulated industries. You'll own the complete infrastructure lifecycle from development to production, ensuring reliability and performance at scale.

What you'll do

  • Design and build inference and model-serving infrastructure for production AI agent deployments
  • Scale systems to handle increasing concurrency and production load reliably
  • Identify and resolve infrastructure bottlenecks in collaboration with ML and platform teams
  • Optimize systems for latency, throughput, and reliability
  • Operate and maintain ML inference platforms through development and deployment phases
  • Monitor and debug production systems using observability tools

What they're looking for

  • ML inference systems and model-serving platforms (TensorFlow Serving, TorchServe, Triton, KServe)
  • Distributed systems design and optimization
  • Kubernetes and Docker containerization
  • Cloud infrastructure (AWS, GCP, or Azure)
  • Monitoring and observability tools (Prometheus, Grafana, ELK, distributed tracing)
  • Systems programming languages (Python, Go, Rust, C++, or Java)
  • Production ML system optimization for latency and throughput
  • Knowledge graphs or graph databases (bonus)
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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

  • Describe your experience designing and scaling inference serving infrastructure in production. What challenges did you face and how did you resolve them?
  • Tell us about a time you optimized an ML system for latency or throughput under high concurrency. What metrics did you track and what was the outcome?
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