AI Engineer (New York)
Edra - New York, New York City, United States
Posted Nov 13, 2025
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Salary
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- 401(k) match
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Schedule
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- Weekend work
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Application
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- Assessment
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- Deadline
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Where they hire
State eligibility is not yet verified.
About this role
AI Engineer (New York) New York, New York City, United States 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, 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 work directly on our core learning library, ship features with real enterprise customers, and contribute back to the platform. The work spans continuous learning systems, agentic features, human-in-the-loop feedback loops, and agent orchestration. What You'll Do - Build and contribute to our core context learning library, implementing new learning capabilities with customers then generalizing them back
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