Senior Machine Learning Scientist – Agentic

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 to enable real‑world task completion.
• Define and own evaluation frameworks for agentic systems, measuring task success, reliability, latency, cost, and safety across diverse benchmarks and production scenarios.
• Collaborate closely 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 in long‑horizon tasks, and unsafe tool use.
• Stay current with the rapidly evolving agentic AI landscape, synthesizing academic research and industry developments to inform the team’s technical direction.
• Mentor junior ML engineers and scientists, providing technical guidance on agentic design patterns, LLM best practices, and experimentation methodology.

• 8+ 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, Tensor Flow, or JAX); experience with LLM APIs and orchestration libraries (e.g., Lang Chain, Llama Index, or similar).
• Experience integrating LLMs with external tools, APIs, and structured data sources for real‑world task completion.
• 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.