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Decagon

Builds AI agents for enterprise customer support

Senior Research Engineer - Safety

AI ResearchUpdated on Nov 10
Full-time

About the Job

Requirements

  • At least 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 machine learning tooling, with strong experimental judgment and engineering depth to ship production systems.
  • Ability to own ambiguous, high-stakes technical problems and make clear risk and product tradeoffs.

Responsibilities

  • 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.

Desired Qualifications

  • Experience building safeguards for high-stakes or regulated enterprise workflows.
  • Familiarity with human-in-the-loop review, incident response, or responsible rollout frameworks for machine learning systems.

About the Company

Decagon

Builds AI agents for enterprise customer support

Decagon builds AI customer support agents for large businesses, handling customer questions and requests over voice, chat, email, and text messages. Its platform connects these conversations to company records and internal tools, so an agent can check an order, change a reservation, or complete a support task. Customer experience teams define how agents should respond and when to pass a case to a person, while testing tools check behavior before changes reach customers. Businesses pay usage fees for the software, with charges tied to conversations or completed resolutions rather than the number of human support staff. Decagon was founded in 2023 and is headquartered in San Francisco.

Company size
501-1,000
Company stage
Series D
Total funding
$481M
Headquarters
San Francisco, CA
Founded
2023

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