ML/AI Research Engineer — Agentic AI Lab (Founding Team)
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Before you apply
SalaryNo pay range posted
LocationSan Francisco Bay Area
Work modeon-site
Typefull-time
SenioritySenior
DepartmentEngineering
EquityIncluded
Company size11–50 people
PostedAug 28, 2025 · 1y ago
Verified live3d ago
May no longer be actively hiring
Posted by the company on Aug 28, 202590% of this company's open roles have been listed for 90+ daysNo pay range listed, though California requires one
At a glanceSummarised by Seekless from the posting.
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
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
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)
Worked with Neo4j, Puppygraph, RDF, OWL, or other semantic modeling systems
Skills
Python
LLaMA 3
Mistral
Falcon
Mixtral
LangChain
LangGraph
LlamaIndex
Weaviate
Qdrant
FAISS
Neo4j
Puppygraph
RDF
HuggingFace Transformers
DeepSpeed
vLLM
FlashAttention
FSDP
LoRA
QLoRA
Ray
Kubernetes
TGI
Sagemaker
LambdaLabs
Modal
DuckDB
Postgres
Parquet
Delta Lake
OpenLLM Evals
Trulens
Ragas
LangSmith
Weight & Biases
Benefits
Equity
ML/AI Research Engineer — Agentic AI Lab (Founding Team)
Location: San Francisco Bay Area
Type: Full-Time
Compensation: Competitive salary + meaningful equity (founding tier)
About the company
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
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Develop embedding-based memory and retrieval chains with token-efficient chunking strategies
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Create reinforcement learning pipelines to optimize agent behaviors (e.g. RLHF, DPO, PPO)
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Establish scalable evaluation harnesses for LLM and agent performance, including synthetic evals, trace capture, and explainability tools
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Contribute to model observability, drift detection, error classification, and alignment
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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
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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
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Startup DNA: resourceful, fast-moving, and capable of working in ambiguity
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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.