Research Scientist

What you'll do

• Invent and validate post-training and RL methods, including reward design, long-horizon planning, tool use, curricula, and data strategy.
• Develop agent memory, evaluation, verification, and enterprise knowledge-representation methods; study multi-agent failure modes.
• Run experiments, turn findings into product capabilities, publish research, and mentor or review others’ work.

What they ask for

• MS or PhD in CS, ML, statistics, math, physics, or a related field, or research experience of comparable depth without the credential.
• Track record of original ML research, such as publications at NeurIPS/ICML/ICLR/ACL-tier venues, influential open-source work, or results that moved a frontier system.
• Deep expertise in at least one of post-training and RL for LLMs, agent memory and long-context reasoning, knowledge representation and structured reasoning, or evaluation methodology.
• Hands-on ability to write code, run experiments, and read logs.

Nice to have

• End-to-end ownership of a research direction at a frontier lab or strong academic group.
• Published work or experience in agents, tool use, RL for LLMs, code generation or repair, reasoning, evaluation, verifiable-reward RL, or knowledge representation.

Pay

$200K – $300K USD base salary

Tools

LLMs, RL

AI focus

Research and develop reliable long-horizon enterprise agents, with emphasis on post-training and RL, memory, evaluation, verification, multi-agent systems, and structured enterprise representations.