Full Time Knowledge Graph Jobs (NOW HIRING)

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.

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.

• 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

• 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 trade‑offs 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

• Bonus: Worked with Neo4j, Puppygraph, RDF, OWL, or other semantic modeling systems

• 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)

• 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)

• 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

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.