Fabrion
ML/AI Research Engineer — Agentic AI Lab (Founding Team)
Fabrion, San Francisco, California, United States, 94199
Overview
Location:
San Francisco Bay Area
Ensure all your application information is up to date and in order before applying for this opportunity. 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 ExperienceModel 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.
#J-18808-Ljbffr
Location:
San Francisco Bay Area
Ensure all your application information is up to date and in order before applying for this opportunity. 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 ExperienceModel 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.
#J-18808-Ljbffr