About the TeamRead 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.
About the RoleAs 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- 4+ years of experience in AI/ML engineering, research, or AI safety
- Hands-on experience evaluating, post-training, or deploying language models or agentic 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
Compensation$200K - $400K + Offers Equity
This range reflects the expected compensation for this role. Compensation within the range is determined based on experience, skills, and the scope of responsibilities, with flexibility for candidates who demonstrate exceptional impact.
In addition to base salary, we offer competitive equity. Final compensation may vary based on location within the United States.
BenefitsWe proudly offer the following benefits for our full-time employees:
- Medical, Dental, and Vision benefits for you and your family
- Life Insurance and Disability Benefits
- Retirement Plan (e.g., 401K, pension)
- Parental Leave
- Fertility and family building benefits through Carrot
- Monthly stipend to support your wellness, lifestyle, and work-life balance
- Daily lunches and snacks in the office to keep you at your best
- Take what you need vacation policy (subject to local requirements; UK employees receive 25 days of statutory leave)
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.
