Staff Research Engineer, Voice + Speech

Decagon Decagon · Vertical AI · New York, NY · Engineering

Staff Research Engineer focused on improving voice and speech capabilities for conversational AI agents in production. The role involves leading research and engineering efforts for end-to-end models and pipelines, optimizing for quality, efficiency, and user experience, and integrating these models into production systems. Requires experience in post-training and deploying LLMs, Python, ML tooling, and a track record of delivering 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)
  • post-training LLMs
  • deploying LLMs in production
  • defining roadmaps
  • leading cross-functional execution

Nice to have

  • speech understanding
  • naturalness
  • turn-taking
  • resilience in real-world conditions
  • instruction following
  • retrieval
  • memory
  • long-horizon task completion

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

  • improving core conversational capabilities in production
  • optimizing models for quality, efficiency, and user experience
  • integrating new models into production systems
  • defining roadmaps and leading cross-functional execution