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

> Onsite · United States · Unspecified · ML / AI · open. Source: https://alderhq.app/jobs/ashby%3Afabrion%3Abf33cfd1-8ca3-4cf9-8fae-e724c3d608fb (read 2026-11-08).

## Facts (observed)

- Company: [Fabrion](https://alderhq.app/companies/fabrion)
- Location text: San Francisco Bay Area
- Where it can be done from (inferred): Onsite · United States (evidence: "Location: San Francisco Bay Area")
- Posted: 2025-08-28 (437 days ago)
- First seen by alder: 2026-11-04 · last verified: 2026-11-08
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://jobs.ashbyhq.com/fabrion/bf33cfd1-8ca3-4cf9-8fae-e724c3d608fb
- Skills listed (inferred): JavaScript, React, Python, Rust, PostgreSQL, Kubernetes, SQL, LLMs

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (SF Bay Area, n 461): p25 $200k, median $250k, p75 $300k

## Description (the employer's text)

### 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

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Afabrion%3Abf33cfd1-8ca3-4cf9-8fae-e724c3d608fb and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.