User Experience Researcher

Job Description

Location: Remote in India Duration: 6 Month Contract to Hire Pay: 2.5-4 lakhs/month Requirements for the Role Experience in behavioral UX design, mapping user flows to cognitive/emotional needs and outcome signals. Skilled in writing prompts and feedback, especially sentiment-rich questions for LLM feedback. Ability to map emotional models using tags like “confidence,” “clarity,” and “relief.” Proficient in UX instrumentation, including front-end tracking hooks, micro-surveys, and telemetry logs. Capable of designing loop responses that trigger reroutes or downstream refinement. Familiarity with Slack and in-app prompt logic, including integration with GitLab/CI. Background in roles such as UX Designer (Senior), UX Researcher, CX Strategist, AI Conversation Designer, Product Designer (AI), or Analytics + UX Hybrid. Nice to Have Experience with emotion-outcome mapping and defining how a feature should “feel.” Ability to write concise UX prompts tied to both emotion and functionality. Strong loop thinking—knowing when feedback should trigger reroutes. Strategic thinking around instrumentation—knowing what to track and why. Collaborative fluency with roles like Intent Designers and Ops Synthesists. Familiarity with sourcing communities such as UX Collective, PromptOps Discord, UXIL Slack, and AI Product London. Day to Day As an AI UX Researcher/Strategist, you will own the UX Feedback Loop, ensuring that each feature delivers not just technical functionality but also behavioral clarity and emotional resonance. Your day will involve designing feedback surfaces, prompts, and signals that help determine whether a feature meets user expectations. You’ll define emotional goals and usage cues, integrate feedback prompts into deployment pipelines, and monitor user sentiment to inform refinements. Collaborating closely with Intent Designers, you’ll help translate user goals into agent-executable formats. You’ll also tag telemetry data to behavioral metrics and contribute summaries of user sentiment and emotional misfires into retrospectives, enabling fast, recursive UX learning. Show more Show less