ML​/AI Research Engineer — Agentic AI Lab; Founding Team

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.

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 Lang Chain, Lang Graph, Llama Index, or Dust — integrated with our vector DBs and internal knowledge graph
• Train agent architectures (ReAct, AutoGPT, BabyAGI, Open Agents) 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 Hugging Face Transformers, Deep Speed, 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, bfloat
  16), and quantization

• Experience building enterprise-grade RAG pipelines integrated with real-time or contextual data
• Familiar with Lang Chain, Lang Graph, Llama Index, 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
  :
  Hugging Face Transformers, Deep Speed, vLLM, Flash Attention, FSDP, LoRA
• Agent Orchestration
  :
  Lang Chain, Lang Graph, ReAct, Open Agents, Llama Index
• 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, Lang Smith, Weight & Biases
• Compute
  :
  Ray, Kubernetes, TGI,…