About The Role

ABOUT THE ROLE

This is a full stack engineering role at an early-stage AI-native consumer intelligence platform, where you'll own features end-to-end — from backend services processing enterprise data at scale to the frontend interfaces that make that intelligence actionable. In a small, fast-moving team, you'll work closely with enterprise customers and ML engineers to ship product that directly shapes how leading brands understand their consumers.

WHAT YOU'LL DO

Build and ship features across the full stack — backend services in Go/Python and frontend experiences in React/TypeScript.
Design APIs, data models, and service architectures that support agentic AI capabilities.
Create intuitive interfaces that translate complex enterprise data into clear, actionable workflows.
Collaborate with ML engineers to bring AI-driven features from prototype to production.
Own features through the full lifecycle: scoping, architecture, implementation, testing, deployment, and iteration.
Work directly with enterprise customers to understand real-world needs and refine the product.
Contribute to infrastructure, tooling, and developer experience as the engineering team scales.

WHAT WE'RE LOOKING FOR

3+ years of professional engineering experience, with at least 2 years in early-stage startup environments shipping end-to-end product.
Proficiency in both TypeScript/React and at least one of Go or Python, with a track record of shipping code in both frontend and backend layers.
Experience with AWS or equivalent cloud infrastructure and modern deployment practices.
Experience building or deploying data pipelines or data engineering work.
Strong product instincts — you think about the user, not just the code.
Comfortable owning loosely-scoped problems and moving from ambiguity to shipped feature independently.
Bachelor's degree in Computer Science or a related field.
Bonus: experience with LLM integrations, agentic AI systems, or RAG architectures; background in enterprise SaaS, retail technology, or data-intensive products; familiarity with data visualization or streaming architectures.