Technical Product Manager, Trust & Safety

The Technical Product Manager, Trust & Safety will define and deliver advanced data products that help leading AI research teams train and evaluate safer, more trustworthy models. This role owns the full product lifecycle, translating research needs into product specifications, reinforcement learning environments, safety rubrics, and evaluation frameworks. A central focus is designing realistic, sandboxed scenarios that expose vulnerabilities such as reward hacking, deception, prompt injection, and unsafe tool use. The successful candidate will establish measurable safety and performance standards while ensuring AI agents can complete tasks responsibly under adversarial conditions. Working closely with researchers, engineers, red-teamers, policy experts, and specialist delivery teams, they will turn emerging risks into practical product improvements. This is an opportunity to shape the development of trustworthy agentic AI in a technically demanding, fast-moving environment.

• At least 5 years of product management experience, including ownership of technical products in machine learning, AI data, cybersecurity, or trust and safety.

• Practical familiarity with large language model training and evaluation methods, including supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), reinforcement learning from AI feedback (RLAIF), reward models, verifiers, and benchmark design.

• Working knowledge of agentic AI systems, including tool use, computer-use agents, multi-step planning, and associated failure modes.

• Strong understanding of AI safety concepts such as reward hacking, deceptive alignment, jailbreaks, and prompt injection.

• Technical fluency sufficient to read code, review environment specifications with engineers, and make informed technical tradeoffs.

• Familiarity with Python and sandboxed environments such as Docker, virtual machines, and browser automation is an advantage.

• Experience designing metrics that measure risk, system performance, and user impact, and using data to guide product decisions.

• Demonstrated ability to launch products from 0 to 1 in fast-paced, ambiguous environments.

• Excellent written and verbal communication skills, with the ability to explain complex technical concepts to researchers, customers, and executives.

• Creative and adversarial problem-solving skills, including the ability to anticipate how advanced AI systems might exploit weaknesses or bypass safeguards.