---
title: 'ML/AI Research Engineer — Agentic AI Lab (Founding Team) at Fabrion'
canonical: 'https://feeny.ai/job/ml-ai-research-engineer-agentic-ai-lab-founding-team-fabrion-san-francisco-m7ry3fmyn2rd'
type: 'job'
last_seen: '2026-09-08'
---

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

- **Company:** Fabrion
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2025-08-28
- **Last confirmed live:** 2026-09-08
- **Apply:** https://jobs.ashbyhq.com/fabrion/bf33cfd1-8ca3-4cf9-8fae-e724c3d608fb

## 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.

## About Fabrion

## Company Overview
- **One-liner**: Fabrion is an AI-native platform and operating system that provides a real-time intelligence layer for industrial manufacturers, transforming complex value chains with agentic AI, knowledge graphs, and data fabrics.
- **Entity Type**: Private – Seed stage (Seed round with one investor)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2025
- **Founders**: Roy Ng (CEO), Kunal B. (CTO & CPO), Christian V. Jordan, Jake Medwell (Board Member) – experienced operators from AWS, Meta, SAP, and Twilio.

## Core Business
- **Primary industries**: Industrial manufacturing, supply chain, enterprise AI
- **Target customers**: B2B – Enterprise industrial manufacturers, OEMs, and tier suppliers
- **Mission/purpose**: “AI will be the operating system for the next industrial era” – building autonomous, governed AI systems that let teams see, decide, and act on complex manufacturing and supply chain data

## Products & Services
- **Fabrion Platform**: An AI-native operating system that ingests, normalizes, and acts on messy, fragmented industrial data. Uses LLMs, knowledge graphs, vector databases, and agentic systems to deliver real-time intelligence.
- **Fabrion AI Lab**: A full-stack vertical AI research platform focused on:
  - Data fabric for AI (metadata-driven cleanse/correlate/stream)
  - Governance-by-design (observable, reversible, compliant AI)
  - Context memory management (short/long-term recall for agents)
  - Industry knowledge graphs (internal data + external signals)
  - Agent mesh architectures (distributed, goal-conditioned agents)
  - Hybrid & multi-cloud infrastructure
  - Custom fine-tuned models (RAG, SLMs, RLHIL, pre-trained enterprise data)

## Market Standing
- **Valuation**: Not disclosed
- **Key metric**: Seed funding round (amount not publicly stated); 1 investor identified (8VC is prominently featured as a partner)
- **Notable investors/partners**: 8VC (lead through “8VC Build” program); partnering with leading OEMs and forward-thinking industrial companies
- **Growth signals**:
  - Workforce of ~7 employees (all founding/early team) with **9 open job postings** across engineering, research, and business development
  - Web traffic growth: +115.2% monthly (4,264 monthly visits)
  - LinkedIn followers: 343 (+7.5% monthly)
  - Founding team has scaled products from zero to hundreds of millions in revenue at previous companies

## Competitive Advantages
- **AI-native from day one** – not a thin wrapper on foundation models; built as a full-stack vertical AI research platform
- **Deep industry partnerships** with OEMs and industrial companies actively modernizing
- **Proven founding team** with experience at AWS, Meta, SAP, and Twilio – domain expertise in manufacturing, logistics, Big Data, SaaS, and enterprise
- **Focus on governance and compliance** – every AI action is explainable, reversible, and measurable, a critical differentiator for regulated industrial environments
- **Knowledge graph + agent mesh approach** – moves beyond static dashboards to autonomous, semi-autonomous agents

## Strategic Focus
- Build autonomous or semi-autonomous AI agents for manufacturing supply chains (self-healing, rebalancing)
- Embed compliance and governance as first-class citizens in the AI stack
- Advance research in data fabrics, context memory, and custom fine-tuned models for industrial verticals
- Expand agentic capabilities from simulation to real-world production environments

## Why Work Here
- **Culture**: “High EQ, low ego, no assholes”; owners not renters; first-principles thinking; win with customer outcomes; score points, not yardage
- **Work modality**: Hybrid – jobs are listed as “In-Office or Remote” with multiple locations (HQ at Pier 5, The Embarcadero, San Francisco; also remote options)
- **Technical stack**: Kubernetes, Keycloak, Python, SQL, Docker, GraphQL, React, TypeScript, Terraform, Snowflake, Neo4j, OpenAI, MLflow, Grafana, Prometheus – cutting-edge AI/ML infrastructure
- **Impact**: Solving mission-critical problems in global trade and manufacturing – value chains representing billions of dollars; opportunity to define a new industrial AI paradigm
- **Team**: Small founding team with deep experience; opportunity to work across engineering, research, and product from the ground up

## Sources
1. [fabrion.com/careers](https://www.fabrion.com/careers)
2. [builtin.com](https://builtin.com/company/fabrion)
3. [jobs.ashbyhq.com](https://jobs.ashbyhq.com/fabrion)
4. [linkedin.com](https://www.linkedin.com/company/fabrionai)
5. [fabrion.com/ai-lab](https://www.fabrion.com/ai-lab)

## Other roles at Fabrion

- [Founding AI Research Lead - Agentic AI Lab](https://feeny.ai/job/founding-ai-research-lead-agentic-ai-lab-fabrion-san-francisco-c251tdtjjvrw) — San Francisco, CA
- [Founding Designer](https://feeny.ai/job/founding-designer-fabrion-san-francisco-zww16cz5wbwb) — San Francisco, CA
- [Data Partnerships Analyst](https://feeny.ai/job/data-partnerships-analyst-fabrion-san-francisco-2h67mf9ssa3z) — San Francisco, CA
- [Business Development Analyst – Auto](https://feeny.ai/job/business-development-analyst-auto-fabrion-san-francisco-4agd1wrm1x3z) — San Francisco, CA
- [DevOps Engineer (Founding Team)](https://feeny.ai/job/devops-engineer-founding-team-fabrion-san-francisco-rhgsyhr9s3kx) — San Francisco, CA
- [Frontend Engineer (Founding Team)](https://feeny.ai/job/frontend-engineer-founding-team-fabrion-san-francisco-p4nbwzbq1kgq) — San Francisco, CA
- [Data Engineer (Founding Team)](https://feeny.ai/job/data-engineer-founding-team-fabrion-san-francisco-33hf28h6rgc7) — San Francisco, CA
- [ML Ops Engineer — Agentic AI Lab (Founding Team)](https://feeny.ai/job/ml-ops-engineer-agentic-ai-lab-founding-team-fabrion-san-francisco-na47hpj9nvs7) — San Francisco, CA
- [Founding Engineer](https://feeny.ai/job/founding-engineer-fabrion-san-francisco-50vz9w20z840) — San Francisco, CA