Principal Machine Learning Scientist - Agentic Experiences

About the job

Responsibilities

• Design, build, and evaluate multi-step agentic AI systems, including autonomous agents capable of planning, tool use, memory management, and multi-agent collaboration.
• Research and implement state-of-the-art techniques in agentic architectures, such as ReAct, reflection loops, chain-of-thought prompting, and tool-augmented reasoning.
• Develop and maintain agent orchestration frameworks, defining how agents decompose tasks, delegate to sub-agents, and handle failure and recovery.
• Integrate large language models (LLMs) with external tools, APIs, databases, and code execution environments for real-world task completion.
• Define and own evaluation frameworks for agentic systems, measuring task success, reliability, latency, cost, and safety.
• Lead collaboration with product, engineering, and research teams to translate business requirements into agentic system designs and deliver production-grade solutions.
• Identify and mitigate risks specific to agentic systems, including prompt injection, unintended actions, hallucination, and unsafe tool use.
• Stay current with the agentic AI landscape, synthesizing academic research and industry developments to inform technical direction.
• Mentor senior ML engineers and scientists on agentic design patterns, LLM best practices, and experimentation methodology.

Requirements

• BS in Computer Science, Machine Learning, Statistics, Engineering, or equivalent professional experience.
• 10+ years of related industry experience.
• Demonstrated experience designing and deploying agentic or multi-step AI systems (e.g., ReAct, tool-calling agents, multi-agent pipelines) in production or research settings.
• Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX).
• Experience with LLM APIs and orchestration libraries (e.g., LangChain, LlamaIndex, or similar).
• Experience integrating LLMs with external tools, APIs, and structured data sources.
• Solid understanding of prompt engineering techniques including chain-of-thought, few-shot prompting, and structured output generation.
• Experience defining and running evaluation frameworks for ML systems, including offline benchmarking and production monitoring.
• Experience taking models from prototype to production in collaboration with Machine Learning Engineering teams.
• Advanced degree (PhD or MS) in Computer Science, Machine Learning, Statistics, Engineering (preferred).
• Experience in the travel or e-commerce industry (preferred).
• Publications in top-tier ML conferences or journals (preferred).
• Patented Inventions, pending and issued (preferred).
• Contributions to open-source ML projects (preferred).

Benefits

• Medical, dental, and vision coverage
• Paid time off
• Employee Assistance Program
• Wellness and travel reimbursement
• Travel discounts
• International Airlines Travel Agent Network (IATAN) membership