Fabrion · 2 months ago
ML/AI Research Engineer — Agentic AI Lab (Founding Team)
San Francisco, CA
Full-time
Onsite
Senior LevelFabrion is backed by 8VC and is building a world-class team to tackle critical infrastructure problems in enterprise AI. The ML/AI Research Engineer will lead the design, training, evaluation, and optimization of agent-native AI models, working at the intersection of LLMs, vector search, graph reasoning, and reinforcement learning.
Artificial Intelligence (AI)Machine Learning
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
Qualification
Required
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
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
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)
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
Preferred
Bonus: Worked with Neo4j, Puppygraph, RDF, OWL, or other semantic modeling systems
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)
Benefits
Competitive salary
Meaningful equity (founding tier)
Company
Fabrion
Fabrion is an AI-native platform purpose-built for the new industrial era
Founded in 2025
2-10 employees
Funding
Current Stage
Early StageTotal Funding
unknown2026-01-01Seed
Company data provided by crunchbase