Staff Research Engineer

Decagon Decagon · Vertical AI · San Francisco, CA · Engineering

Staff Research Engineer at Decagon, a conversational AI platform company. The role focuses on building and deploying industry-leading conversational AI models and agents, from idea to production. Responsibilities include improving core conversational capabilities, building end-to-end models and pipelines, and mentoring other researchers. Requires 8+ years of experience in AI/ML, prior experience post-training and deploying LLMs, and a track record of production impact.

What you'd actually do

  1. Lead research and engineering efforts to improve core conversational capabilities in production, including instruction following, retrieval, memory, and long-horizon task completion
  2. Build and iterate on end-to-end models and pipelines that optimize for quality, efficiency, and user experience
  3. Partner with platform and product engineers to integrate new models into production systems
  4. Break down ambiguous research ideas into clear, iterative milestones and roadmaps.
  5. Mentor other researchers/engineers, set technical direction, and establish best practices for applied research and engineering

Skills

Required

  • Python
  • modern ML tooling (training, evaluation, data pipelines)

Nice to have

  • instruction following
  • retrieval
  • memory
  • long-horizon task completion
  • prompting
  • orchestration
  • evaluation

What the JD emphasized

  • 8+ years of experience in AI/ML engineering or research
  • Prior experience post-training and deploying LLMs in production environments
  • Track record of taking research ideas from prototype → reliable, measurable production impact
  • Ability to define a roadmap, break ambiguity into milestones, and lead cross-functional execution

Other signals

  • building core models and algorithms
  • taking them all the way from idea to production
  • multi-quarter initiatives
  • push the agent's reliability, capability, and efficiency forward
  • design and implement frontier approaches for training, evaluation, and orchestration