Software Engineer - India

At Dscout, we’re building the most flexible and powerful UX research platform on the market—trusted by the world’s top brands in finance (JP Morgan Chase, Intuit, Charles Schwab, PayPal), healthcare (Aya, Headspace), consumer goods (Keen, Verizon, Target, Northface), and tech (Google, Amazon, Facebook, Meta, Spotify, AirBnB). Our tools help teams deeply understand the humans behind their products, so they can build better ones. We are expanding our smart and driven team and would love for you to join us.

Building AI-native products is a different problem than building deterministic software: the same input won't always produce the same output. Good product engineering here means designing experiences that stay useful, trustworthy, and easy to understand even when the AI underneath doesn't behave the same way twice — and knowing when that's a product design problem, not just a model problem.

We're looking for a Software Engineer with 2-7 years of experience who thinks like a product owner, not just an implementer. You're comfortable being handed a vague, half-formed problem and figuring out what's actually worth building. You default to empathy for the researcher or participant on the other end of the screen, and you'd rather ship something real and learn from it than wait for a perfect spec. You're fluent enough with modern LLM-based systems to build good product experiences on top of them, even if tuning the model itself isn't your job.

What you'll do

Own end-to-end delivery of product features — from an ambiguous problem statement to shipped, working software real researchers and participants use

Partner directly with Product and Design to help define what should be built, not just how to build it

Use our design system to make sound, independent calls on smaller UX and interaction decisions, and know when a change is big enough to loop Design in

Build the product surfaces (web app flows, dashboards, in-product controls) that make AI-driven behavior understandable, controllable, and trustworthy for the people using it

Integrate LLM-based features and agent outputs into real product flows — treating them as a building block you design around, not infrastructure you need to own

Talk to users directly when you need to; bring what you learn back into design and prioritization decisions

Ship fast, instrument what you ship, and iterate based on real usage rather than a fixed spec

Collaborate with AI-focused engineers on deeper model or agent tuning when a feature's behavior depends on it

Sweat the craft: interaction design, edge cases, error states, and how the feature actually feels to use

What you bring

2-7 years of software engineering experience shipping full-stack product features to real users

Comfortable working across the stack (frontend and backend) and picking up whatever's needed to ship

A track record of taking a vague, underspecified problem and shipping something real without a fully-scoped ticket

Genuine empathy for users: you default to understanding what someone is actually trying to do, not just what the ticket says

Working fluency with LLM-based systems: you've built features on top of LLM APIs or agents and understand prompting and non-determinism well enough to design good product experiences around them

Strong product judgment: you can reason about tradeoffs (build vs. buy, LLM vs. deterministic logic, speed vs. polish) and push back when a request doesn't serve the user

Comfortable using our design system to independently make good UX calls on smaller details, without needing Product or Design sign-off for every decision

Comfort using AI coding tools (Cursor, Claude Code, Copilot, or similar) as a real part of your workflow

High agency and a bias toward shipping

Nice to have