Senior AI Engineer - Systems & Architecture

Role Overview

We're seeking a Senior AI Engineer to join our fast-paced team to design, develop, and implement new features for

Littlebird

, our personalized AI teammate for Mac and Android. We’re building personal AI; a thought partner that connects dots in your work and helps you think. We're a small, async-first team that values craft and ownership. 

The Role

You will architect the system that allows our agent to reason, remember, and evolve. You'll develop core infrastructure that turns messy, unstructured user data into a coherent world model.

Some of the hard problems you'll solve:

Build a Real AI Memory (Beyond Vectors):

Our current vector-based memory is hitting its limits. You'll lead the charge to design and implement our next-generation memory system This means solving hard problems in:

Entity & Relationship Extraction:

How do you reliably extract "my sister Anna" and the "Q4 roadmap" as distinct entities and link them from unstructured text, at scale?

Temporal Awareness:

How do you model facts that change over time? A user's project status, their job title, their relationship to a contact—these aren't static. You'll build the systems to manage this temporal complexity.

Graph-based Retrieval:

How do you query a graph efficiently to provide the agent with rich, structured context, moving beyond simple semantic search?

Architect a Scalable, Debuggable Agentic System:

Our agent is a multi-step, tool-using system.

You'll design clean interfaces between the agent's "brain" (the planner) and its "hands" (the tools/modalities).

You'll build the debugging and observability tools necessary to understand

why

an agentic flow failed, which is one of the hardest challenges in this space.

Scale Our Data & Retrieval Pipelines:

As we grow, you'll be responsible for ensuring our data ingestion, hybrid search (BM25 + vector), and re-ranking pipelines are fast, reliable, and cost-effective.

Required Skills

Deep experience building and scaling data-intensive backend systems in Python.

A strong mental model for data structures and their trade-offs. You like to think deeply about how to represent complex information.

Hands-on experience with RAG pipelines, but you're probably a little frustrated with their limitations.

A pragmatic approach. You know when to build for the long term and when to ship a clever solution that works now.

Benefits

Remote-friendly work environment

Collaborative team culture

Opportunity to shape infrastructure decisions

Competitive compensation packages including stock and health benefits, paid time off, and parental leave

Flexible working hours across multiple time zones