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Clera

Software Engineer, Voice AI

San Francisco$150k–$225kfulltimemidAdded yesterday

About this role

Join a Y Combinator-backed robotics AI startup to build cloud systems powering humanoid robot voice interactions. You'll develop low-latency, production-grade pipelines that enable robots to understand and respond naturally to people in real-world deployments.

What you'll do

  • Design and develop cloud-side voice AI systems for real-time robot-human interaction
  • Build and optimize composite voice pipelines integrating speech and language models
  • Test features on production humanoid robots and iterate based on field deployments
  • Implement knowledge storage/retrieval, turn-taking logic, and personality tuning for conversational AI
  • Reduce interaction latency and improve naturalness of robot conversations
  • Deploy and operate services across GCP and AWS edge locations using Python backends

What they're looking for

  • Voice AI development and pipeline architecture
  • Python 3.x backend development
  • Real-time latency-sensitive media systems
  • AWS or GCP cloud infrastructure and Kubernetes
  • UDP-based streaming protocols
  • Observability and monitoring (Prometheus, Grafana, distributed tracing)
  • CI/CD practices and infrastructure automation
  • Dialog systems and conversational AI

Benefits

  • Salary: $150,000–$225,000 annually
  • Visa sponsorship available
  • Work on production robots deployed in real-world environments
  • High ownership in a small, fast-moving team
  • On-site San Francisco location with cutting-edge robotics tech
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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 voice AI pipeline you've built—what were the latency bottlenecks and how did you optimize them?
  • Tell us about a time you deployed software to customer-facing devices or robots in the field. How did you handle production issues?
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