ML/AI Research Engineer — Agentic AI Lab (Founding Team)
Fabrion · San Francisco Bay Area
About this role
## ML/AI Research Engineer — Agentic AI Lab (Founding Team) **Location:** San Francisco Bay Area **Type:** Full-Time **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**. You’ll join our AI Lab to lead the design, training, evaluation, and optimization of **agent-native AI models**. You’ll work at the intersection of **LLMs**, **vector search**, **graph reasoning**, and **reinforcement learning**—building the intelligence layer on top of our enterprise data fabric. This is **full-cycle ML** (not a prompt engineering role): from **data curation and fine-tuning** to **evaluation, interpretability, and deployment**, with **cost-awareness, alignment, and agent coordination** all in scope. --- ## Core Responsibilities - **Fine-tune and evaluate** open-source LLMs (e.g., LLaMA 3, Mistral, Falcon, Mixtral) for enterprise use cases with structured + unstructured data - **Build and optimize RAG pipelines** using LangChain, LangGraph, LlamaIndex, or Dust—integrated with vector DBs and an 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/agent performance (synthetic evals, trace capture, 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 --- ## Desired Experience ### Model Training - Deep experience fine-tuning open-source LLMs using HuggingFace Transformers, DeepSpeed, vLLM, FSDP, LoRA/QLoRA - Worked with base + instruction-tuned models; familiar with SFT and 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 ### RAG + Knowledge Graphs - Experience building enterprise-grade RAG pipelines integrated with real-time/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: Neo4j, Puppygraph, RDF, OWL, or other semantic modeling systems ### Agent Intelligence - Experience training/customizing agent frameworks with multi-step reasoning and memory - Understand agent loop patterns (e.g., Plan→Act→Reflect), memory recall, and tools - Familiar with self-correction, multi-agent communication, and agent ops logging ### Optimization - 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) --- ## Preferred Tech Stack - **LLM Training & Inference:** HuggingFace Transformers, DeepSpeed, vLLM, FlashAtt
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