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Decagon is the leading conversational AI platform empowering every brand to deliver concierge customer experiences. Our technology enables industry-defining enterprises to deploy AI agents that power personalized, deeply satisfying interactions across voice, chat, email, SMS, and every other channel. We’re an in-office company, driven by a shared commitment to excellence and velocity. Our values — Just Get It Done, Invent What Customers Want, Winner’s Mindset, and The Polymath Principle — shape how we work and grow as a team. The Research team develops the model and decision-making stack that powers Decagon’s conversational agents for enterprise support. We research, adapt, and implement state-of-the-art techniques in model training, prompting, orchestration, and evaluation in order to make our agents more accurate, robust, and efficient in real-world deployments. Our goal is to push the frontier of applied conversational AI: agents that reliably understand nuanced intent, track long context, and take the right actions under uncertainty. We measure success the way customers feel it: higher resolution rates, better user satisfaction, and consistent behavior at scale. As a Research Engineer, you’ll be responsible for building industry-leading conversational AI models that power Decagon’s agent, and taking them all the way from idea to production. You’ll own multi-quarter initiatives that push the agent’s reliability, capability, and efficiency forward, and you’ll design and implement frontier approaches for training, evaluation, and orchestration across the system. We’re looking for strong engineers who want to build the core models and algorithms behind our AI agents. People here own their work end-to-end, ship real improvements, and are trusted to make high-impact technical decisions. In this role, you will Lead research and engineering efforts to improve core conversational capabilities in production, including instruction following, retrieval, memory, and long-horizon task completion Build and iterate on end-to-end models and pipelines that optimize for quality, efficiency, and user experience Partner with platform and product engineers to integrate new models into production systems Break down ambiguous research ideas into clear, iterative milestones and roadmaps. Your background looks something like this 2+ years of experience in AI/ML engineering or research. Prior experience post-training and deploying LLMs in production environments. Fluency in Python and modern ML tooling (training, evaluation, data pipelines) Track record of taking research ideas from prototype → reliable, measurable production impact Compensation These benefits are described in more detail in Decagon’s policies, may vary by location, and can change at any time according to applicable compensation and benefits plans.