Principal AI/ML Researcher / Engineer Reasoning, Planning, and Decision-making systems
Description:
- Drive foundational and applied research in reasoning engines, planning architectures, and decision-making frameworks at scale.
- Advance LLM/LRM post-training, reinforcement learning–based decisioning, and knowledge-integrated agents.
- Design methods for plan induction, value estimation, and contingency modeling within intelligent agents.
- Explore and validate protocols for distributed reasoning and joint planning among cooperative agents.
- Architect systems that integrate post-trained LLMs/LRMs, graph-structured memory, and RL-driven controllers.
- Design recursive task planners, search-based or policy-based reasoners, and belief-state trackers.
- Define communication protocols, coordination strategies, and cross-agent knowledge alignment mechanisms for multi-agent systems.
- Build and evolve stateful models that combine supervised learning with online/offline reinforcement, simulation-based rollouts, and symbol grounding.
- Set direction for planning and reasoning infrastructure within the AI/ML platform strategy.
- Work across product, infrastructure, design, researchers, ontologists, and ML engineers to translate ambiguous product intent into multi-stage reasoning pipelines.
- Productionize real-time reasoning loops with low-latency inference, caching, retrieval-augmented generation, and streaming updates to symbolic memory.
- Create monitoring, attribution, and evaluation pipelines for agent behavior and decision quality.
Requirements:
- Master’s degree or equivalent in Computer Science, AI, Cognitive Science, or a related field.
- Recent published work or patents in AI, Cognitive Science, or related fields.
- 15+ years of experience in AI/ML, including post-training architectures and production-scale reasoning systems.
- Advanced coding proficiency in Java, Python, C++, or similar languages.
- Experience with ML/RL frameworks such as PyTorch, Ray, JAX, or RLlib at scale.
- Proven experience integrating LLMs/LRMs with Knowledge Graphs or structured world models.
- Deep understanding of Reinforcement Learning and its application to decisioning and planning.
- Fluency in hybrid model architectures such as connectionist-symbolic fusion, retrieval-based agents, or goal-directed transformers.
- Experience working on multi-agent coordination, distributed RL, or cooperative inference systems.
- Ph.D. in AI, Machine Learning, Robotics, Cognitive Systems, or a related area preferred.
- Published work or patents in multi-agent reasoning, plan synthesis, knowledge-augmented learning, or generative control preferred.
- Experience with cognitive architectures, neuro-symbolic systems, or agent-based simulation environments preferred.
- Demonstrated ability to lead cross-functional research-to-production transitions preferred.
- Experience with memory architectures, task graphs, or semantic program induction preferred.
- Prior work on distributed intelligence platforms with explicit agent interaction models and collective decision-making logic preferred.
- Must live in a state where Airbnb, Inc. has a registered entity for this US-remote-eligible role.
Benefits:
- Base pay range of $296,000 to $370,000 USD.
- Eligible for bonus compensation.
- Eligible for equity.
- Eligible for benefits.
- Eligible for Employee Travel Credits.
- US-remote-eligible with occasional office or offsite attendance as agreed with the manager.
- Reasonable accommodations are available for candidates with disabilities during the application and interview process.
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