LLM Integration Engineer

Job Description

Requirements
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### Must have:

### - We bring at least 2 years of experience in backend engineering, AI automation, or complex systems integration. - We have demonstrated success building and maintaining production-ready software with clear modular boundaries, such as separate services for parsing, business logic, and reporting. - We are proficient in at least two major programming languages, such as Python, JavaScript, Go, or Java, and we have experience working with SQL databases. - We have practical experience delivering solutions in live, non-simulated environments and managing multi-step system interactions. - We are highly detail-oriented and able to deliver concise, high-signal technical feedback on sophisticated system behavior. - We bring additional strength in designing multi-stage coordination workflows where information gathering leads to reasoned output. - We have hands-on experience connecting agents to live tools and APIs, such as Supabase and Gmail, to solve real-world use cases. - We are comfortable implementing persistent state and session tracking using MEMORY.md to monitor agent progress. - We can identify subtle issues such as privacy leakage, authority escalation, and indirect prompt injection.

Responsibilities:
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- We help train advanced generative systems by providing high-quality human feedback. - We collaborate with leading AI teams to improve large language models so they can operate as proactive, multi-step agents. - We contribute to projects that teach systems how to design, coordinate, and refine complex real-world architectural workflows. - We evaluate system behavior in live scenarios and provide detailed observations on performance and failure modes. - We support the development of multi-turn agent interactions and help improve orchestration across tasks and tools. - We assess complex coordination flows and offer precise technical feedback to strengthen reliability and output quality.
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Company:
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We are Outlier, and we help innovative companies improve their AI agents through human feedback. Our work focuses on training large language models to behave as proactive, multi-step agents capable of designing, coordinating, and optimizing complex architectural workflows in the real world. We partner with leading AI organizations and are looking for people who are excited to help shape the future of autonomous systems.
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