Job Opportunities API

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