Research Engineer, Safety

• 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 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

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