Clera
Backend-Leaning Product Engineer
About this role
Join a small, early-stage startup as a Backend-Leaning Product Engineer to own customer-facing features end-to-end in AI and automation products. You'll make independent product and engineering decisions, build backend systems and integrations, and work closely with founders in a high-ownership, fast-moving environment.
What you'll do
- Own customer-facing features from design through launch and iteration
- Design and maintain backend systems, APIs, data models, and automation pipelines
- Integrate with external tools, legacy systems, and third-party services
- Make independent product and engineering decisions with minimal PM support
- Debug production issues and apply strong judgment under pressure
- Collaborate with founders, customers, and cross-functional stakeholders
What they're looking for
- Backend software engineering and system design
- API design and implementation
- Full-lifecycle feature ownership
- Third-party system integration
- Python (preferred)
- Workflow orchestration and AI/LLM integration experience
- Production debugging and troubleshooting
- Cross-functional communication
Benefits
- Meaningful early-stage equity package
- High ownership and autonomy in product decisions
- Work directly with founders and customers
- Hybrid work environment in San Francisco
- Fast-moving, high-impact role at early-stage startup
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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.
View all jobs at CleraLikely interview questions
- Tell us about a time you owned a customer-facing feature end-to-end from conception to iteration—what was the most challenging trade-off you had to make?
- Describe your experience integrating with external systems or legacy platforms. What made those integrations particularly messy, and how did you solve it?