The posting, in Decagon's own words
archived Sep 4, 2026About Decagon Decagon is the leading conversational AI platform empowering every brand to deliver concierge customer experiences. Our technology enables industry-defining enterprises like Avis Budget Group, Block’s Cash App and Square, Chime, Oura Health, and Hunter Douglas to deploy AI agents that power personalized, deeply satisfying interactions across voice, chat, email, SMS, and every other channel. We’re building a future where customer experiences are being redefined from support tickets and hold music to faster resolutions, richer conversations, and deeper relationships. We’re proud to be backed by world-class investors who share that vision, including a16z, Accel, Bain Capital Ventures, Coatue, and Index Ventures, along with many others. 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.
About the Team
Read more about the research team's work here: https://decagon.ai/blog/introducing-decagon-labs 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.
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About the Role
As a Research Engineer focused on Safety, you’ll be responsible for making Decagon’s AI agents safe, reliable, and controllable, from evaluation through production. You’ll identify real-world failure modes and build the models, evaluations, and safeguards that prevent them. We’re looking for strong engineers who want to advance applied AI safety in production. 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 Research and build safeguards against prompt injection, unsafe tool use, sensitive-data disclosure, policy violations, and hallucinated commitments Build adversarial evaluations, simulations, red-team datasets, and regression suites informed by production failures Develop and deploy classifiers, judges, reward signals, post-training methods, and runtime safeguards for safer agent behavior Analyze production traces and incidents to identify root causes, test mitigations, and measure their impact Partner with Security, Product, Infrastructure, Legal, and customer-facing teams to turn enterprise requirements into scalable safeguards and rollout practices Your background looks something like this 2+ years of experience in AI/ML engineering, research, or AI safety Hands-on experience evaluating, post-training, or deploying language models or systems Experience with modern post-training techniques, such as reinforcement learning, preference optimization, distillation, model routing, and synthetic-data generation Experience with adversarial testing, model red teaming, prompt injection, policy enforcement, privacy, or safe tool use Fluency in Python and modern ML tooling, with strong experimental judgment and the engineering depth to ship production systems Comfort owning ambiguous, high-stakes technical problems and making clear risk and product tradeoffs Even better if you have Experience building safeguards for high-stakes or regulated enterprise workflows Familiarity with human-in-the-loop review, incident response, or responsible rollout frameworks for ML systems