E
AI Engineer (New York)
Edra Inc.
2 hours ago
Full-time
On-site
About Edra
Edra is solving one of the hardest problems in enterprise AI: AI models are generic but company processes are specific. We build AI agents that learn how processes actually run, and then run their operations. We're a Series A startup, backed by Sequoia and other leading VC firms, led by Co-Founders who created and led Forward Deployed AI Engineering at Palantir, and we're growing our team in New York and London. We're a deeply technical team of engineers, AI researchers, and strategists with a high bar for talent and a shared belief that exceptional people are the foundation of everything great we'll build. The Role
We're building a learning system that teaches AI agents how enterprises actually work. Our system ingests knowledge bases, conversations, tickets, and system logs, then produces written instructions that agents can execute–with confidence scoring to know when to automate and when to seek human input. We're looking for AI Engineers who have built complex, production LLM-based systems. Whether you've scaled LLM workflows handling millions of requests, built multi-agent systems in production, or designed evaluation frameworks for enterprise deployments, we want people who bring intensity and self-direction to their craft. You’ll spend most of your time advancing our core learning library and building the harness that powers our platform’s agents across all customers. The work spans continuous learning systems, agentic features, human-in-the-loop feedback loops, and agent orchestration. What You'll Do
Build and extend our core context-learning library, turning customer-informed problems into reusable platform capabilities.
Design and implement LLM-powered systems and agentic workflows from concept to production
Build agentic features for knowledge management–think agents that can edit, update, and maintain large knowledge bases autonomously
Build reliability and confidence systems–evaluation frameworks, confidence scoring, and logic for when to automate vs. when to elevate to a human
Architect async, scalable systems that handle complex AI orchestration
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Edra is solving one of the hardest problems in enterprise AI: AI models are generic but company processes are specific. We build AI agents that learn how processes actually run, and then run their operations. We're a Series A startup, backed by Sequoia and other leading VC firms, led by Co-Founders who created and led Forward Deployed AI Engineering at Palantir, and we're growing our team in New York and London. We're a deeply technical team of engineers, AI researchers, and strategists with a high bar for talent and a shared belief that exceptional people are the foundation of everything great we'll build. The Role
We're building a learning system that teaches AI agents how enterprises actually work. Our system ingests knowledge bases, conversations, tickets, and system logs, then produces written instructions that agents can execute–with confidence scoring to know when to automate and when to seek human input. We're looking for AI Engineers who have built complex, production LLM-based systems. Whether you've scaled LLM workflows handling millions of requests, built multi-agent systems in production, or designed evaluation frameworks for enterprise deployments, we want people who bring intensity and self-direction to their craft. You’ll spend most of your time advancing our core learning library and building the harness that powers our platform’s agents across all customers. The work spans continuous learning systems, agentic features, human-in-the-loop feedback loops, and agent orchestration. What You'll Do
Build and extend our core context-learning library, turning customer-informed problems into reusable platform capabilities.
Design and implement LLM-powered systems and agentic workflows from concept to production
Build agentic features for knowledge management–think agents that can edit, update, and maintain large knowledge bases autonomously
Build reliability and confidence systems–evaluation frameworks, confidence scoring, and logic for when to automate vs. when to elevate to a human
Architect async, scalable systems that handle complex AI orchestration
#J-18808-Ljbffr