About Cargobase
Cargobase is changing how enterprise shippers like Bosch, Reckitt Benckiser, Goodyear, and Textron, along with logistics providers like DHL, DSV, and FedEx, run spot-buy freight. This is a multi-billion dollar corner of logistics, and we're bringing the whole process online: quoting, tracking, invoice audit, and data insights.
The Role
We're looking for an experienced, hands-on full-stack engineer who loves shipping across the stack and working AI-natively. You'll be a core builder on our small, high-trust team, helping us build agentic AI right into the product. You'll write the features our enterprise customers rely on and help set the technical bar for how we build and review code.
Key Responsibilities
Technical Ownership & Execution
- Ship meaningful full-stack features end to end, from design through to production, spanning both the UI and the services behind it.
- Bring agentic AI into the product (for instance, extracting structured data from unstructured sources like emails and documents) and help figure out where AI genuinely makes the experience better.
- Keep the codebase healthy through pragmatic architecture, sensible data modeling across relational and NoSQL stores, and a steady habit of tackling tech debt.
Process & Operational Excellence
- Work AI-natively by using agentic coding tools like Cursor or Claude Code fluently, applying them with smart, context-aware judgment rather than brute force.
- Help shape the engineering practices and standards that keep us shipping quickly and reliably.
Internal & Cross-Functional Collaboration
- Level up the whole team through thoughtful, high-quality code reviews, setting the bar for how we build and review.
- Work closely with teammates and partners across Product and QA, turning real customer needs into solid technical solutions.
What We're Looking For
- You have over 3-5 years of experience shipping production software as a full-stack engineer, with a genuine balance between frontend and backend work.
- On the frontend, you're strong with React and TypeScript, with enough depth to guide and review frontend work effectively.
- On the backend, you're strong with Python and have experience with Django, FastAPI, or Flask.
- You're comfortable with data modeling across both relational and NoSQL databases.
- Working with Docker and in containerized environments like Kubernetes feels like second nature to you.
- You already work AI-natively, or you have a clear enthusiasm and fluency for that way of working.
Nice to Have
- It's a bonus if you've built agentic or LLM features into a product: things like RAG, vector search, structured extraction, or agent tool-calling, even if just through side projects.
- Experience with AWS (especially EKS), CI/CD ownership, and observability tools is definitely a plus.
- A background in enterprise B2B SaaS is great, especially if it's in logistics or supply chain.
Our Stack
We use Python (Django, FastAPI, Flask), React, and TypeScript. Our data lives in relational and NoSQL databases, and we run on Docker and AWS (EKS) with solid CI/CD. We also love AI-native tools like Cursor and Claude Code.
