Senior Applied Scientist - AI Agent Systems
Core
Design and deploy production-grade AI agents capable of structured reasoning, planning, and decision-making using classical algorithms combined with LLMs.
Role type
Senior Applied Scientist (AI Agent Systems)
Builds
Autonomous workflows, decision engines, and tool-driven agent ecosystems
Domain
Artificial Intelligence, Classical Planning, Search Algorithms, LLM Integration
Deliverable
production ML models
Required skills
Monte Carlo Tree Search (MCTS), heuristic search, graph-based planning, state-space representation, tool-calling architecture design, Python engineering, system design for latency/cost constraints
Preferred skills
reinforcement learning, multi-agent systems, evaluation framework design, LLM inference optimization, distributed task orchestration
Technologies
Monte Carlo Tree Search, beam search, A* search, vector-based memory, Python
Responsibilities
Design modular AI agent frameworks with skill decomposition and persistent state tracking; Implement planning and search algorithms for complex decision-making; Develop decision-making loops balancing exploration, cost, and latency; Build structured memory systems with optimized retrieval strategies; Design tool-calling architectures with execution validation and failure recovery; Develop evaluation frameworks for agent performance and reliability
Seniority
Senior, hands-on IC