Fabrion

Fabrion

ML/AI Research Engineer — Agentic AI Lab (Founding Team)

San Francisco Bay Area · Full-time

Sponsorship not specifiedDetected 327 days ago
JavaScriptPythonReactSQLPostgreSQLVector DatabasesKubernetesMachine LearningNLPLLMsRAGAgentic AILangGraphAI OrchestrationA/B TestingResearchCommunication

About the role

  • We're designing the future of enterprise AI infrastructure - grounded in agents, retrieval-augmented generation (RAG), knowledge graphs, and multi-tenant governance.
  • It's full-cycle ML: from data curation and fine-tuning to evaluation, interpretability, and deployment - with cost-awareness, alignment, and agent coordination all in scope.
  • Fine-tune and evaluate open-source LLMs (e.g. LLaMA 3, Mistral, Falcon, Mixtral) for enterprise use cases with both structured and unstructured data

Responsibilities

  • Build and optimize RAG pipelines using LangChain, LangGraph, LlamaIndex, or Dust - integrated with our vector DBs and internal knowledge graph
  • Develop embedding-based memory and retrieval chains with token-efficient chunking strategies

Nice to have

  • Worked with Neo4j, Puppygraph, RDF, OWL, or other semantic modeling systems
  • Strong background in token cost optimization, chunking strategies, reranking (e.g. Cohere, Jina), compression, and retrieval latency tuning
  • Experience running models under quantized (int4/int8) or multi-GPU settings with inference tuning (vLLM, TGI)

Skills

  • Train agent architectures (ReAct, AutoGPT, BabyAGI, OpenAgents) using enterprise task data
  • Establish scalable evaluation harnesses for LLM and agent performance, including synthetic evals, trace capture, and explainability tools
  • Contribute to model observability, drift detection, error classification, and alignment
  • Experience training or customizing agent frameworks with multi-step reasoning and memory
  • Understand common agent loop patterns (e.g. Plan→Act→Reflect), memory recall, and tools
  • Familiar with self-correction, multi-agent communication, and agent ops logging
  • Python (core), optionally Rust (for inference layers) or JS (for UX experimentation)
  • Soft Skills & Mindset

Compensation

  • Competitive salary + meaningful equity (founding tier)
  • Backed by 8VC, we're building a world-class team to tackle one of the industry's most critical infrastructure problems.
  • About the Role
  • We're designing the future of enterprise AI infrastructure - grounded in agents, retrieval-augmented generation (RAG), knowledge graphs, and multi-tenant governance.
  • We're looking for an ML/AI Research Engineer to join our AI Lab and lead the design, training, evaluation, and optimization of agent-native AI models.
  • This isn't a prompt engineer role.

Benefits

  • Bonus: Worked with Neo4j, Puppygraph, RDF, OWL, or other semantic modeling systems
  • You'll work at the intersection of LLMs, vector search, graph reasoning, and reinforcement learning - building the intelligence layer that sits on top of our enterprise data fabric.
  • Create reinforcement learning pipelines to optimize agent behaviors (e.g. RLHF, DPO, PPO)

Company info

  • We're looking for an ML/AI Research Engineer to join our AI Lab and lead the design, training, evaluation, and optimization of agent-native AI models.
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