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

Fabrion· Engineering
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San Francisco Bay AreaFull-timePosted 11mo ago

About the role

ML/AI Research Engineer to design, train, evaluate, and optimize agent-native AI models at the Agentic AI Lab founding team. Focus areas include LLMs, vector search, graph reasoning, and reinforcement learning to build an enterprise data-driven intelligence layer with RAG, knowledge graphs, and multi-tenant governance.

Responsibilities

  • Fine-tune and evaluate open-source LLMs (e.g. LLaMA 3, Mistral, Falcon, Mixtral) for enterprise use cases with both structured and unstructured data
  • Build and optimize RAG pipelines using LangChain, LangGraph, LlamaIndex, or Dust — integrated with vector DBs and internal knowledge graph
  • Train agent architectures (ReAct, AutoGPT, BabyAGI, OpenAgents) using enterprise task data
  • Develop embedding-based memory and retrieval chains with token-efficient chunking strategies
  • Create reinforcement learning pipelines to optimize agent behaviors (e.g. RLHF, DPO, PPO)
  • 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
  • Optimize inference latency and GPU resource utilization across cloud and on-prem environments

Qualifications

  • Deep experience fine-tuning open-source LLMs using HuggingFace Transformers, DeepSpeed, vLLM, FSDP, LoRA/QLoRA
  • Worked with both base and instruction-tuned models; familiar with SFT, RLHF, DPO pipelines
  • Comfortable building and maintaining custom training datasets, filters, and eval splits
  • Understand tradeoffs in batch size, token window, optimizer, precision (FP16, bfloat16), and quantization
  • Experience building enterprise-grade RAG pipelines integrated with real-time or contextual data
  • Familiar with LangChain, LangGraph, LlamaIndex, and open-source vector DBs (Weaviate, Qdrant, FAISS)
  • Experience grounding models with structured data (SQL, graph, metadata) + unstructured sources
  • Bonus: Worked with Neo4j, Puppygraph, RDF, OWL, or other semantic modeling systems
  • Experience training or customizing agent frameworks with multi-step reasoning and memory
  • Understand common agent loop patterns (Plan→Act→Reflect), memory recall, and tools
  • Familiar with self-correction, multi-agent communication, and agent ops logging
  • Strong background in token cost optimization, chunking strategies, reranking, compression, and retrieval latency tuning
  • Experience running models under quantized (int4/int8) or multi-GPU settings with inference tuning

Skills

Required:Fine-tuning open-source LLMsRAG pipelinesAgent architecturesMemory and retrieval optimizationReinforcement learning pipelinesEvaluation harnessesInference optimization

Benefits

  • Competitive salary + meaningful equity (founding tier)
Last updated Sep 9, 2026. Interested? Apply on the company site.

About Fabrion

AI-native platform purpose-built for the new industrial era

AI-native platform purpose-built for the new industrial era

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