Decagon
Research Engineer
About this role
Research Engineer role at Decagon, a conversational AI platform company, focused on developing and deploying production-grade LLM-based models for enterprise customer support agents. You'll own end-to-end initiatives to improve agent reliability, capability, and efficiency through research in training, evaluation, and orchestration techniques.
What you'll do
- Lead research and engineering efforts to enhance conversational capabilities including instruction following, retrieval, memory, and task completion
- Design and build end-to-end models and pipelines optimized for quality, efficiency, and user experience
- Integrate new models into production systems in partnership with platform and product engineers
- Break down research ideas into clear iterative milestones and multi-quarter roadmaps
- Evaluate and measure impact of improvements on production metrics like resolution rates and user satisfaction
What they're looking for
- LLM post-training and production deployment
- Python and modern ML tooling
- Model training and evaluation frameworks
- Data pipeline engineering
- Conversational AI and NLP
- System design and optimization
- Research-to-production translation
Benefits
- Flexible unlimited vacation policy
- Medical, Dental, and Vision coverage for employees and families
- Life Insurance and Disability Benefits
- 401K/Retirement plan
- Parental Leave and fertility 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
- Can you walk through a specific LLM project you took from research prototype to production and what metrics you optimized for?
- How have you approached improving model efficiency and reliability in production conversational AI systems?