Decagon
Research Engineer
About this role
Decagon seeks a Research Engineer to develop and deploy state-of-the-art conversational AI models powering enterprise customer support agents. You'll own end-to-end research initiatives—from prototype to production—improving agent reliability, capability, and efficiency across real-world deployments.
What you'll do
- Lead research and engineering efforts to improve conversational capabilities including instruction following, retrieval, memory, and task completion
- Build and iterate on end-to-end models and pipelines optimizing for quality, efficiency, and user experience
- Partner with platform and product engineers to integrate new models into production systems
- Break down research ideas into clear, iterative milestones and technical roadmaps
- Design and implement frontier approaches for training, evaluation, and orchestration
- Measure and drive production impact through resolution rates and user satisfaction metrics
What they're looking for
- AI/ML engineering and research
- Large Language Model (LLM) post-training and deployment
- Python programming
- ML tooling (training frameworks, evaluation, data pipelines)
- Model evaluation and optimization
- Production systems integration
- Long-context understanding and memory systems
- Conversational AI and NLP
Benefits
- Flexible vacation policy
- Medical, dental, and vision coverage for family
- Life insurance and disability benefits
- Retirement plan (401K/pension)
- Parental and fertility/family building benefits
- Daily office lunches and snacks
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Decagon
Decagon builds enterprise-grade conversational AI platforms that enable organizations to deploy AI agents for business impact. The company is hiring Strategic Solutions Engineers, Customer Engineers, Platform Engineers, and systems-focused engineers to deliver AI implementations, build internal infrastructure, and establish security practices across their growing platform.
View all jobs at DecagonLikely interview questions
- Describe a time you took an LLM research idea from prototype to production—what were the key challenges and how did you measure impact?
- How do you approach building evaluation metrics for conversational AI systems, especially for nuanced tasks like instruction following or long-context memory?