Asks for

The ad says“4 or more years of experience working with LLMs, machine learning infrastructure, or applied AI systems”
The ad says“Experience building and scaling data or ML pipelines in production environments”
The ad says“Deep understanding of retrieval-augmented generation, embeddings, and vector databases (such as Pinecone, Weaviate, or FAISS)”
The ad says“Familiarity with evaluation frameworks, feedback loops, and reinforcement learning techniques”
The ad says“Deep understanding of retrieval-augmented generation, embeddings, and vector databases (such as Pinecone, Weaviate, or FAISS)”
The ad says“Familiarity with evaluation frameworks, feedback loops, and reinforcement learning techniques”
The ad says“Strong experience with Python and frameworks like LangChain, LlamaIndex, or equivalent orchestration tools”
The ad says“Strong experience with Python and frameworks like LangChain, LlamaIndex, or equivalent orchestration tools”
The ad says“4 or more years of experience working with LLMs, machine learning infrastructure, or applied AI systems”
The ad says“4 or more years of experience working with LLMs, machine learning infrastructure, or applied AI systems”
The ad says“Experience building and scaling data or ML pipelines in production environments”
The ad says“Deep understanding of retrieval-augmented generation, embeddings, and vector databases (such as Pinecone, Weaviate, or FAISS)”
The ad says“Strong experience with Python and frameworks like LangChain, LlamaIndex, or equivalent orchestration tools”
The ad says“Familiarity with evaluation frameworks, feedback loops, and reinforcement learning techniques”
The ad says“Deep understanding of retrieval-augmented generation, embeddings, and vector databases (such as Pinecone, Weaviate, or FAISS)”
The ad says“Deep understanding of retrieval-augmented generation, embeddings, and vector databases (such as Pinecone, Weaviate, or FAISS)”
The ad says“Deep understanding of retrieval-augmented generation, embeddings, and vector databases (such as Pinecone, Weaviate, or FAISS)”

About Babs

Babs is building the smart operating system for modern families. Most AI tools are designed for work life, but managing home life is its own full-time job filled with coordination, communication, and constant context switching. Babs acts as a second brain that brings order to everyday life by connecting calendars, tasks, and messages into one intelligent system.

Our edge is in our ability to make complex systems feel simple. Our strength is our team — driven, thoughtful builders united by a mission to bring order to everyday life and give people the clarity of an organized mind. Our mission is to give people the power to live organized lives and organized minds. Our vision is to build joyful, connected communities. That begins with creating systems and workflows that help people feel more present and effective in their daily lives. When households run smoothly, they have more capacity to connect, contribute, and strengthen their communities.

The Role

We’re looking for an Founding AI Engineer to design, build, and scale the intelligence layer that powers Babs. You’ll work on the infrastructure that connects large language models, vector databases, and real-world data into a seamless, adaptive system.

This role sits at the intersection of machine learning infrastructure, data systems, and applied product engineering. You’ll be responsible for how Babs learns, remembers, and improves — turning context into intelligence and feedback into reinforcement.

You’ll help us move from AI-assisted features to a truly AI-native product that feels personal, contextual, and trustworthy.

What You’ll Do

Design and implement retrieval and memory systems using vector databases and semantic search
Build and maintain RAG pipelines that combine structured and unstructured data
Develop feedback loops and evaluation systems to measure and improve model output quality
Explore reinforcement learning (RL) and fine-tuning approaches that adapt model behavior to user context
Integrate and experiment with multiple LLMs and APIs, choosing the right tool for each task
Collaborate with platform and product engineers to bring intelligence into real features
Create observability and evaluation systems for AI latency, accuracy, and reliability
Contribute to architectural decisions that shape Babs’ long-term AI infrastructure

Our Ideal Candidate Has

4 or more years of experience working with LLMs, machine learning infrastructure, or applied AI systems
Strong experience with Python and frameworks like LangChain, LlamaIndex, or equivalent orchestration tools
Deep understanding of retrieval-augmented generation, embeddings, and vector databases (such as Pinecone, Weaviate, or FAISS)
Familiarity with evaluation frameworks, feedback loops, and reinforcement learning techniques
Experience building and scaling data or ML pipelines in production environments
Curiosity about user behavior and how models can be tuned to better serve real human needs
A thoughtful, practical approach to experimentation and iteration — you ship and learn

Bonus Points For

Experience designing model evaluation frameworks or AI Evals
Contributions to open-source AI infrastructure projects
Familiarity with prompt optimization, tool calling, and agentic workflows
Experience with multi-modal models (text, image, or speech)
Knowledge of GCP, Firebase, or event-driven architectures
A background in consumer or productivity products

Compensation & Benefits

Base salary range: $150,000 – $225,000 and equity, depending on experience and expertise
Competitive compensation package including equity
Comprehensive medical, dental, and vision coverage
Flexible PTO and a work culture built on trust and autonomy
A team that values craftsmanship, collaboration, and purpose over fluff

If you’re excited by the idea of building the intelligence layer behind a product that helps people live with more clarity and calm, we’d love to meet you — even if you don’t check every box. We care about curiosity, integrity, and a shared belief in what we’re building.