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R&D Engineer - AI and Innovation

13 hours, 11 minutes ago
Canada
Mid Level
AI Research Scientist
Software Development
Neo4j Python Vertex AI
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ZoomInfo

ZoomInfo

ZoomInfo provides a comprehensive B2B database that offers accurate and actionable contact and company intelligence, enabling sales and marketing teams to enhance their campaigns and improve prospecting efforts through access to direct dial phone numbe...

Professional Services
1K-5K
Founded 2000
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Description

  • Monitor and synthesize developments in LLM systems, AI infrastructure, and related research areas.
  • Prototype and evaluate new ideas quickly through a build, demo, and iterate workflow.
  • Extend and improve the existing agent and RAG SDKs with new retrieval strategies, agent patterns, and provider integrations.
  • Evaluate embedding models, reranking methods, and retrieval architectures against the current pipeline.
  • Collaborate with Platform and AI Engineers to hand off validated prototypes for productionization.
  • Create clear documentation and demos that make research understandable to non-researchers.
  • Partner with the tech lead on long-term research direction and technical investment decisions.
  • Engage with the broader research community through conferences, papers, and open-source projects.
  • Identify opportunities for emerging techniques to solve real problems surfaced by AI Engineers.

Requirements

  • Hands-on experience building or evaluating LLM systems such as fine-tuning, inference, retrieval, or agents.
  • Familiarity with advanced RAG architectures beyond naive vector search, including HyDE, MMR, graph-based retrieval, reranking, or hybrid search.
  • Exposure to agent frameworks and multi-agent orchestration patterns such as PydanticAI, LangGraph, AutoGen, or CrewAI.
  • Demonstrated ability to move quickly from idea to working prototype.
  • A portfolio of explored ideas, such as published work, technical blog posts, side projects, or open-source contributions.
  • Strong communication skills and the ability to explain complex research to non-researchers clearly.
  • A collaborative mindset and understanding that research value is realized when ideas ship.
  • Core experience with Python, agent frameworks, RAG pipeline design, and managed ML platforms such as Vertex AI or equivalent.
  • Nice-to-have experience with systems-level programming, inference infrastructure such as vLLM or TensorRT, RLHF or fine-tuning pipelines, MCP protocol, or graph databases such as Neo4j.

Interested in this position?

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