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.