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Описание вакансии
Текст:
TL;DR
Research Engineer (AI Safety) (Conversational AI): Building models, evaluations, and runtime safeguards that make enterprise conversational agents safer, more reliable, and controllable with an accent on adversarial evaluation, post-training, and production safety. Focus on detecting prompt injection, unsafe tool use, sensitive-data disclosure, policy violations, and hallucinated commitments while analyzing incidents and deploying scalable mitigations.
Location: In-office in San Francisco or New York City, United States
Salary: $200,000–$400,000 per year plus equity
Company
Decagon develops a conversational AI platform that enables enterprises to deliver personalized customer experiences across voice, chat, email, SMS, and other channels.
What you will do
- 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 based on production failures.
- Develop and deploy classifiers, judges, reward signals, post-training methods, and runtime safeguards for safer agent behavior.
- Analyze production traces and incidents, identify root causes, test mitigations, and measure impact.
- Partner with Security, Product, Infrastructure, Legal, and customer-facing teams to develop scalable safeguards and rollout practices.
Requirements
- 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 reinforcement learning, preference optimization, distillation, model routing, or 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 the engineering depth to ship production systems.
- Ability to own ambiguous, high-stakes technical problems and make clear risk and product tradeoffs.
Nice to 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.
Culture & Benefits
- In-office work environment focused on excellence and velocity.
- Medical, dental, vision, life insurance, and disability benefits for employees and families.
- Retirement plan, parental leave, and fertility and family-building benefits.
- Monthly wellness and lifestyle stipend.
- Daily office lunches and snacks.
- Take-what-you-need vacation policy, subject to local requirements.
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