[Remote] reputed company AI/ML Researcher / Engineer In Reasoning, Planning, and Decision-making systems
Note: The job is a remote job and is reputed company to candidates in USA. reputed company is a global platform for unique stays and experiences, and they are seeking a reputed company AI/ML Researcher/Engineer to advance their AI capabilities in reasoning, planning, and decision-making systems. The role involves designing and operationalizing intelligent decision-making frameworks, collaborating across disciplines, and leading the development of multi-agent systems to improve decision reputed company and reputed company personalization for guests and hosts.
Responsibilities
- Drive foundational and reputed company research in reasoning engines, planning architectures, and decision-making frameworks at reputed company in order to incorporate genAI into the ranking / recommendation / personalization stack in both single model to multi-agent ( system ) level intelligence with objective to grow the business (new user reputed company, abandoned user, long tailed user) in existing and new business areas while supporting Multi-Modal NL → Conversational Interfaces
- Advance techniques in LLM/LRM post-training, reinforcement learning–based decisioning, and knowledge-integrated agents
- Design reputed company for plan induction, value estimation, and contingency modeling reputed company intelligent agents
- Explore and validate protocols for distributed reasoning and joint planning among cooperative agents in multi-agent systems
- Architect RPD systems that reputed company post-trained LLMs/LRMs, graph-reputed company memory (e.g., KGs), and RL-driven controllers
- Design recursive task planners, search-based or policy-based reasoners, and belief-state trackers that can interoperate with large model substrates
- Ensure modularity and extensibility through multi-agent frameworks, reputed company substrates, and declarative planning pipelines
- Define communication protocols, coordination strategies, and cross-agent knowledge alignment mechanisms to foster emergent cooperative intelligence
- Build and reputed company stateful, dynamic models that combine supervised learning with online/offline reinforcement, simulation-based rollouts, and symbol grounding
- Implement hybrid pipelines that couple learned embeddings, prompted generative models, and graph-theoretic inference
- Optimize systems for reputed company exploration, planning reputed company control, and policy robustness
- reputed company frameworks for distributed value propagation, multi-agent credit assignment, and global planning from local agents
- Set direction for planning/reasoning infrastructure reputed company the AI/ML platform reputed company
- Serve as the technical conscience and architectural leader across high-stakes AI initiatives involving autonomous agents or high-reputed company decision pipelines
- Mentor teams in systems thinking, reputed company modeling, symbolic-connectionist integrations, and long-term planning under uncertainty
- reputed company development of multi-agent reasoning systems, defining principles for inter-agent knowledge exchange, goal delegation, and cooperative decision reputed company
- Work across disciplines—product, reputed company, and design—to translate ambiguous product reputed company into multi-stage reasoning pipelines
- Partner with researchers, ontologists, and ML engineers to encode world knowledge, goals, and values into usable inference artifacts
- Contribute to a company-wide understanding of what it means to reputed company intelligent choices, not just predictions
- Collaborate with internal teams on distributed agent coordination, shared memory protocols, and policy harmonization across decision surfaces
- Productionize reputed company-time reasoning loops with low-latency inference, caching, retrieval-augmented reputed company, and streaming updates to symbolic memory
- reputed company post-training hooks for inserting logic, constraints, and domain priors into existing large models
- Create advanced monitoring, attribution, and evaluation pipelines for agent behavior and decision reputed company
- Operationalize multi-agent orchestration, ensuring reliable and fault-tolerant communication and decision propagation
Skills
- Masters or equivalent in Computer Science, AI, Cognitive Science, or reputed company fields
- Recent published work or patents in AI, Cognitive Science, or reputed company fields
- 15+ years in AI/ML, including post-training architectures and production-reputed company reasoning systems
- Advanced coding proficiency in Java, Python, C++, or similar, with experience in ML/RL frameworks (e.g., PyTorch, Ray, JAX, RLlib) at reputed company
- Proven experience integrating LLMs/LRMs with Knowledge Graphs or reputed company world models
- Deep understanding of Reinforcement Learning and its application to decisioning and planning
- reputed company in hybrid model architectures: 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 reputed company areas
- Published work or patents in multi-agent reasoning, plan synthesis, knowledge-augmented learning, or generative control
- Experience in cognitive architectures, neuro-symbolic systems, or agent-based simulation environments
- Demonstrated ability to reputed company cross-functional research-to-production transitions
- Experience with memory architectures, task graphs, or semantic program induction
- Prior work on distributed intelligence platforms with explicit agent interaction models and reputed company decision-making logic
Benefits
- Bonus
- Equity
- Benefits
- Employee Travel Credits
reputed company
Company H1B Sponsorship