Full Stack Engineer

Full Stack Engineer - LA or SF

Millee is the fastest-growing, AI-driven recruitment performance platform in the world.

Built on 10,000 real won and lost recruitment processes, Millee’s data shows users what creates success or causes failure in their searches. We join that data with deep context about our customers to find the opportunities worth acting on, and then put agents to work: automating risk analysis, drafting responses, auditing deals for nuance, to run a superior search. It measures what works and gets better every day.

As our Engineer, an early-stage seat with founder level equity, you will build and optimize the infrastructure that powers our AI-driven insights. You will design scalable APIs, process large datasets efficiently, and ensure high-performance systems that help businesses take control of their AI recruitment performance.

Preferred Requirements:

Production experience

They must have shipped AI features to production and be comfortable owning architecture and core product reliability from day one.

Systems builder

They need to be a strong full stack engineer who can turn messy real world workflows into reliable software systems.

Architecture ownership

They will design and evolve a serverless, event driven architecture that processes recruiting activity data in real time.

AI systems design

They need experience building multi step AI systems using orchestration frameworks such as Mastra or similar agent frameworks.

Model routing judgment

They must understand how to route tasks across different model tiers, using large models for generation and smaller models for classification and structured reasoning.

Data pipeline engineering

They should be comfortable building event ingestion pipelines that process webhooks, transcripts, and CRM activity into structured signals.

Retrieval systems

They need experience working with retrieval systems that combine proprietary knowledge bases with context aware AI generation.

Reliability mindset

They must design systems with strong failure isolation, retry handling, and durable queues so individual failures never break the pipeline.

Scaling product intelligence

They will be responsible for improving and scaling the AI decision layer that assembles context, diagnoses recruiting situations, retrieves expertise, and generates recommended actions for users.