AI/ML Engineer — Multi-Agent Systems

Role overview Design and build the systems that help AI agents coordinate on complex, multi-step work and retain useful context over time. The role combines applied machine learning engineering with research into agent reasoning, retrieval, and reliability.

Responsibilities - Create coordination methods that let multiple agents divide and complete complex tasks. - Build retrieval-augmented generation pipelines to support agent memory and persistent context. - Adapt and optimize language models for operational tasks and reasoning workflows. - Develop ways to evaluate agent performance and reliability. - Work with product colleagues to turn user needs into agent capabilities.

Requirements - At least three years of AI or machine learning engineering experience, focused on NLP or multi-agent systems. - Strong Python skills and experience with deep learning frameworks such as PyTorch or JAX. - Familiarity with language model orchestration tools and prompt engineering. - Understanding of vector storage and semantic retrieval architectures. - A master's or doctoral degree in computer science or AI, or equivalent industry experience.

Nice to have - Experience with reinforcement learning or autonomous systems. - Research published at leading AI conferences. - Experience deploying large-scale machine learning models in production.

Benefits and work setup - Full-time, remote role.