# ML/AI Research Engineer — Agentic AI Lab \(Founding Team\) - Company: [Fabrion]() - Location: San Francisco Bay Area - Team: Engineering - Employment type: Full Time - Posted: August 28, 2025 ## Job description # **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. 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. 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. This isn’t a prompt engineer role. 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. **Core 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 our 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 **Desired Experience** **Model Training:** * 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 **RAG + Knowledge Graphs:** * 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 **Agent Intelligence:** * 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 **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, FlashAttention, FSDP, LoRA * **Agent Orchestration**: LangChain, LangGraph, ReAct, OpenAgents, LlamaIndex * **Vector DBs**: Weaviate, Qdrant, FAISS, Pinecone, Chroma * **Graph Knowledge Systems**: Neo4j, Puppygraph, RDF, Gremlin, JSON-LD * **Storage & Access**: Iceberg, DuckDB, Postgres, Parquet, Delta Lake * **Evaluation**: OpenLLM Evals, Trulens, Ragas, LangSmith, Weight & Biases * **Compute**: Ray, Kubernetes, TGI, Sagemaker, LambdaLabs, Modal * **Languages**: Python (core), optionally Rust (for inference layers) or JS (for UX experimentation) **Soft Skills & Mindset** * Startup DNA: resourceful, fast-moving, and capable of working in ambiguity * Deep curiosity about **agent-based architectures** and real-world enterprise complexity * Comfortable owning model performance end-to-end: from dataset to deployment * Strong instincts around explainability, safety, and continuous improvement * Enjoy pair-designing with product and UX to shape capabilities, not just APIs **Why This Role Matters** This role is foundational to our thesis: that **agents + enterprise data + knowledge modeling** can create intelligent infrastructure for real-world, multi-billion-dollar workflows. Your work won’t be buried in research reports — it will be productionized and activated by hundreds of users and hundreds of thousands of decisions. If this is your dream role - we would love to hear from you. ## Apply [Apply on Fabrion]() Canonical job page: