Job description

Role Overview We're seeking a Senior/Staff AI Engineer(s) to join our fast-paced team to design, develop, and implement new features for Littlebird , our personalized AI teammate for Mac and Android (Windows soon!). We’re building personal AI; a thought partner that connects dots in your work and helps you think. Check out the recent TechCrunch news on our $11M seed funding and what users are saying about the product. We're an async-first team that values craft and ownership. Our engineers live at the intersection of genuine research curiosity and production discipline: you'll push the frontier in areas like conversational inference, memory, and sentiment analysis, then wrestle those ideas into systems that are fast, lean, and built to scale. 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 • We love to hear when birds chirp!

Requirements

Must-haves

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.
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.
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.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aipython

Platform: gem
Effort: medium

Benefits

Benefits detected in the listing.

Paid Time Off

Parental Leave