We are seeking an exceptional AI Agent Algorithms Engineer to design, develop, and deploy intelligent autonomous systems. You will work at the intersection of large language models (LLMs), reinforcement learning, multi-agent systems, and distributed computing to build next-generation AI agents that can perceive, reason, plan, and act in complex environments.
Key Responsibilities
Design and implement core algorithms for AI agents, including perception, decision-making, planning, and execution modules. Develop and optimize multi-agent collaboration mechanisms, task decomposition, and tool-use frameworks. Research and integrate state-of-the-art LLM-based reasoning methods (e.g., chain-of-thought, ReAct, Reflexion) into agent architectures. Build robust memory systems (episodic, semantic, procedural) and knowledge retrieval mechanisms for long-term agent performance. Design reward models and fine-tune agents using reinforcement learning from human feedback (RLHF) and self-play. Collaborate with cross-functional teams (product, backend, data) to deploy agents at scale with low latency and high reliability. Continuously evaluate agent performance, identify failure modes, and iterate on algorithmic improvements.
Required Qualifications
Education: M.S. or Ph.D. in Computer Science, AI/ML, or a related field (or equivalent industry experience). Experience: 3+ years of hands-on experience in LLM-based agent development, reinforcement learning, or autonomous systems.
Technical Skills:
Proficiency in Python and deep learning frameworks (PyTorch, TensorFlow, or JAX). Strong understanding of transformer architectures, LLM fine-tuning (LoRA, PPO, DPO), and prompt engineering. Experience with agent frameworks (LangChain, AutoGen, CrewAI, or custom implementations). Familiarity with vector databases (Pinecone, Weaviate, Milvus) and retrieval-augmented generation (RAG). Knowledge of distributed systems, message queues (Kafka, RabbitMQ), and cloud infrastructure (AWS/GCP/Azure). Research: Track record of publishing in top-tier conferences (NeurIPS, ICML, ICLR, ACL) or open-source contributions to AI/agent ecosystems. Problem-Solving: Strong analytical skills with the ability to break down ambiguous problems and design scalable solutions.
Preferred Qualifications
Experience with multi-agent simulations (e.g., NetLogo, MASON) and emergent behavior analysis. Hands-on experience with RL frameworks (Ray RLLib, Stable Baselines3) and reward shaping. Familiarity with neuro-symbolic AI, program synthesis, or formal verification methods. Background in robotics, game AI, or autonomous driving systems. Contributions to open-source agent tools (e.g., Hugging Face Agents, LangChain).
What We Offer
Competitive salary and equity package. Opportunity to work on cutting-edge AI agent research with direct real-world impact. Access to large-scale compute resources and proprietary datasets. Collaborative, innovation-driven culture with regular paper discussions and hackathons. Flexible work environment and comprehensive benefits.
How to Apply
Please submit your resume, GitHub portfolio, and a brief description of your most impactful agent-related project.