Applied AI Research Engineer

About Enterpret

Enterpret is redefining how businesses understand and act on customer feedback. We are building an AI-native platform that centralises feedback from every source surveys, reviews, support tickets, communities and turns it into clear, actionable insights that drive business growth.
We are trusted by some of the world’s most customer-obsessed companies like Canva, Descript, Notion, Perplexity and many more, and backed by leading investors like Kleiner Perkins, Canaan Partners, and Peak XV Partners. Our mission: to unlock the voice of the customer for every product team on the planet.

About the Role

This is a hands-on role for someone who’s equally strong in research thinking and engineering execution. You will define what “good” looks like for AI-powered features, build systems that meet that bar, and own them through launch and beyond. From writing eval plans to debugging failures in production, you will work across the stack and across functions to ship reliable, high-quality LLM-driven systems.


What You Will Do ?

You will design, build & ship AI-backed features that are reliable in production

• Define the quality bar: design eval rubrics, test plans, and rollout criteria. Make sure they’re measurable and enforced.
• Build with real-world constraints: write and extend production code, set up monitoring, and add tests that catch regressions before users do.
• Own features end to end from problem framing to modeling, from system design to rollout and iteration.
• Debug failures across the stack including data, infra, model, prompt logic and harden the system with what you learn.
• Design and implement systems: retrieval pipelines, agents, or hybrid patterns, based on what the problem actually needs.
• Work across functions: collaborate with product, infra, and engineers to ship features that actually stick.

• Strong research instincts: you are good at defining what “working” means and designing evaluations that reflect real-world usage.
• Solid engineering skills: you write clean, testable Python, debug at system boundaries, and know your way around production stacks.
• LLM understanding: you have worked with modern models and know how to prompt, fine-tune, or wrap them with tooling and evaluation.
• Systems mindset: you think in interfaces, data contracts, failure modes, and rollout plans, not just model tweaks.
• Practical bias: you care more about what ships and survives than what’s novel.
• Ownership: you take initiative, communicate clearly, and push for quality without being asked.

• We build AI systems that people can trust because they have been tested, monitored, and hardened through real usage.
• We care deeply about quality. Eval plans, incident retros, and per-tenant guardrails aren't checkboxes, they're the core of how we build.
• We believe research belongs in production, not just papers. You will see your ideas live in running systems within weeks, not years.
• We are small enough that every engineer matters, and focused enough that there's no busywork, just high-impact problems.
• You will be part of a team that runs toward hard, ambiguous challenges and sees them through to working, reliable systems.
• There is no playbook here, we are writing it as we go. If that excites you, not scares you, you will thrive here.