Senior Software Engineer (Product)

Software Engineer – AI-Powered Product Automation London - Hoxton (Onsite) Join a seed-stage AI startup turning messy ops data into automation that actually works. Ex-Palantir team, $6.5M funded, real traction. Build full-stack, ship fast, and shape how AI gets used in the real world.

This isn't your typical frontend/backend role. We are looking for pruduct minded engineers. It’s a chance to build core infrastructure for how AI gets used in real companies, messy edge cases and all.

The team (ex-Palantir, Stanford, Yale) is building a platform that turns unstructured operational chaos, Slack threads, emails, tickets, into structured, agent-ready workflows. They’re already embedded in critical sales, support, and ops systems at enterprise customers.

This is early-stage in the best way: high trust, high autonomy, fast feedback loops, and a product that's already solving real pain. You’ll be working closely with founders and users, and your code won’t just ship, it’ll shape the direction of the company.

What you’ll be doing

• Building full-stack features with Python (FastAPI) and TypeScript (Next.js)

• Designing interfaces that help users understand, edit, and trust what the AI is doing

• Working directly with customers to learn how they operate and feeding that insight back into the product

• Debugging across AWS, shaping infra with Terraform, and making systems reliable

• Shipping fast, raising the bar, and caring deeply about product quality

What makes you a good fit

• You're strong in Python, TypeScript, or both

• You’ve gone deep into something—could be games, compilers, trading systems, robotics, obscure open-source projects

• You thrive in early-stage chaos and don’t wait for permission to fix things

• You care more about building something valuable than chasing the perfect abstraction

• You like being around smart, kind people and believe good products come from tight, in-person collaboration

Why this team and why now?

Everyone’s building AI agents—but most are brittle, overconfident, and wrong. The missing piece is context: the institutional knowledge, messy edge cases, and hard-won processes that make businesses actually function. This team is solving that problem.

They're not building a thin wrapper around GPT. They're building the connective tissue between enterprise reality and AI potential. As foundation models get better, the product gets more useful, not less.

If you want to do high-leverage engineering, learn fast, and see your work land in production within days, this is one of those rare roles that hits all three.

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