Job Description

Founding Machine Learning Engineer – Transformers, RAG, and Agentic Systems

We’re working with a stealth startup backed by one of Silicon Valley’s top investors, building foundational infrastructure for AI systems that reason, retrieve, and interact — not just generate.

This is an opportunity to join as a founding engineer focused on transformer-based architectures, evaluation pipelines, and LLM-native agents. You’ll be part of a small, senior team solving hard problems around performance, grounding, and generalization in LLM-based systems.

What You’ll Be Working On:

• Fine-tuning and optimizing transformer models for real-world task performance (instruction tuning, LoRA, QLoRA)
• Designing and deploying RAG pipelines using vector stores, custom embedding strategies, and multi-hop retrieval
• Architecting agentic systems that can reason, plan, and use tools through language interfaces (LangChain/LangGraph-style frameworks)
• Developing robust evaluation frameworks to measure hallucination, factuality, retrieval relevance, and tool use accuracy
• Working with deeply technical founders on building from first principles — no legacy baggage, no corporate layers

You Might Be a Fit If You:

• Have shipped or trained LLMs, or have gone deep into adapting/optimizing them (e.g. LLaMA, Falcon, Mixtral, Phi)
• Are fluent with modern LLM tooling – HuggingFace Transformers, DeepSpeed, vLLM, PyTorch Lightning
• Understand how to build and evaluate retrieval-augmented generation (RAG) systems using FAISS, Chroma, or Pinecone
• Have built or contributed to tool-using agents or function-calling workflows, and know the edge cases
• Are comfortable building in a lean, highly technical environment with direct ownership and high ambiguity

Why This Role?

• You’ll be building the core of something truly ambitious — not another LLM wrapper, but real systems intelligence
• You’ll work with a team that cares deeply about technical rigor, practical grounding, and speed of execution
• And you’ll have the chance to define not just the system, but the standard for how LLMs are evaluated, used, and trusted

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