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Fabrion

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

LocationSan Francisco Bay Area
Work modeon-site
Typefull-time
DepartmentEngineering
EquityIncluded
Company size1+ people
First seen1w ago
Last seen1d ago
ML/AI Research Engineer — Agentic AI Lab (Founding Team)
Benefits
Location: San Francisco Bay Area
Type: Full-Time
Compensation: Competitive salary + meaningful equity (founding tier)
About the company
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
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Fine-tune and evaluate open-source LLMs (e.g. LLaMA 3, Mistral, Falcon, Mixtral) for enterprise use cases with both structured and unstructured data
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Build and optimize RAG pipelines using LangChain, LangGraph, LlamaIndex, or Dust — integrated with our vector DBs and internal knowledge graph
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Train agent architectures (ReAct, AutoGPT, BabyAGI, OpenAgents) using enterprise task data
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Develop embedding-based memory and retrieval chains with token-efficient chunking strategies
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Create reinforcement learning pipelines to optimize agent behaviors (e.g. RLHF, DPO, PPO)
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Establish scalable evaluation harnesses for LLM and agent performance, including synthetic evals, trace capture, and explainability tools
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Contribute to model observability, drift detection, error classification, and alignment
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Optimize inference latency and GPU resource utilization across cloud and on-prem environments
Desired Experience
Model Training:
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Deep experience fine-tuning open-source LLMs using HuggingFace Transformers, DeepSpeed, vLLM, FSDP, LoRA/QLoRA
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Worked with both base and instruction-tuned models; familiar with SFT, RLHF, DPO pipelines
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Comfortable building and maintaining custom training datasets, filters, and eval splits
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Understand tradeoffs in batch size, token window, optimizer, precision (FP16, bfloat16), and quantization
RAG + Knowledge Graphs:
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Experience building enterprise-grade RAG pipelines integrated with real-time or contextual data
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Familiar with LangChain, LangGraph, LlamaIndex, and open-source vector DBs (Weaviate, Qdrant, FAISS)
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Experience grounding models with structured data (SQL, graph, metadata) + unstructured sources
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Bonus: Worked with Neo4j, Puppygraph, RDF, OWL, or other semantic modeling systems
Agent Intelligence:
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Experience training or customizing agent frameworks with multi-step reasoning and memory
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Understand common agent loop patterns (e.g. Plan→Act→Reflect), memory recall, and tools
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Familiar with self-correction, multi-agent communication, and agent ops logging
Optimization:
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Strong background in token cost optimization, chunking strategies, reranking (e.g. Cohere, Jina), compression, and retrieval latency tuning
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Experience running models under quantized (int4/int8) or multi-GPU settings with inference tuning (vLLM, TGI)
Preferred Tech Stack
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LLM Training & Inference: HuggingFace Transformers, DeepSpeed, vLLM, FlashAttention, FSDP, LoRA
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Agent Orchestration: LangChain, LangGraph, ReAct, OpenAgents, LlamaIndex
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Vector DBs: Weaviate, Qdrant, FAISS, Pinecone, Chroma
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Graph Knowledge Systems: Neo4j, Puppygraph, RDF, Gremlin, JSON-LD
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Storage & Access: Iceberg, DuckDB, Postgres, Parquet, Delta Lake
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Evaluation: OpenLLM Evals, Trulens, Ragas, LangSmith, Weight & Biases
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Compute: Ray, Kubernetes, TGI, Sagemaker, LambdaLabs, Modal
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Languages: Python (core), optionally Rust (for inference layers) or JS (for UX experimentation)
Soft Skills & Mindset
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Startup DNA: resourceful, fast-moving, and capable of working in ambiguity
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Deep curiosity about agent-based architectures and real-world enterprise complexity
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Comfortable owning model performance end-to-end: from dataset to deployment
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Strong instincts around explainability, safety, and continuous improvement
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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.
More roles at Fabrion
Founding EngineerDevOps Engineer (Founding Team)Founding AI Research Lead - Agentic AI LabData Partnerships AnalystML Ops Engineer — Agentic AI Lab (Founding Team)
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