Research Scientist - [Seed Model - Alignment & Preparedness]

• Strong foundations in ML, deep learning, and statistics, with research or engineering experience in LLMs, RL, or related fields.
• Proficiency in Python and PyTorch (or similar), with the ability to independently implement algorithms, build experiment pipelines, and debug data, training, and evaluation issues.
• Deep hands-on experience in at least one of the following: agent RL, model training, dataset development, agent systems, evaluation, or ML infrastructure.
• Ability to turn open-ended questions about model behavior into testable hypotheses, run experiments, and clearly communicate results and limitations to research, engineering, and domain partners.
• A genuine commitment to advancing safety alongside capabilities, and comfort with ambiguous, unsolved problems.

• Experience with agent RL, including environment design, reward design for multi-step tasks, and long-horizon or multi-agent training.
• Experience with SFT, RLHF/RLAIF/RLVR, distributed training, or automated red teaming.
• Research experience in AI control, scalable oversight, CoT monitoring, mechanistic interpretability, or prompt injection defense.
• Experience evaluating or mitigating risks in high-stakes domains such as CBRN or cybersecurity.
• Strong publications, open-source contributions, or a track record of applying research to real-world systems.

Seed Model–Alignment & Preparedness is dedicated to advancing AI safety alongside frontier model capabilities. Backed by sustained investment and substantial compute resources, we tackle fundamental challenges in alignment, safety evaluation, and preparedness for emerging risks—translating research into safe, reliable AI systems that benefit people.

We’re looking for researchers to make frontier models and agents safer, with a strong focus on agent RL: training agents that stay safe and controllable in tool use, code execution, and long-horizon tasks.