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.