UX Researcher II

Job Description

Job Description : UX Researcher, AI & LLM Products

Team : Foundation Model Group, Prescient Design (AI for Drug Discovery)

The Foundation Model group within Prescient Design is seeking an exceptional UX Researcher. Our team is at the forefront of developing AI and agentic frameworks to revolutionize drug discovery. We are specifically looking for candidates with direct experience in LLMs (Large Language Models) and AI agents, and / or a background in biotech, pharma, or a related industry, with a proven track record of shipping internal software products. As the bridge between our AI development team and our users-world-class scientists and biomedical experts-your mission is to ensure our agentic framework is not only powerful but also intuitive and seamlessly integrated into the complex workflows of cutting-edge drug discovery. You will champion a user-centered approach, translating deep user insights into a product that accelerates scientific breakthroughs. The Hiring Manager is prioritizing profiles with experience in LLMs / agents and a background in biotech, pharma, or a related field, specifically those who have shipped an internal build, software, or product.

The Role

• In this role, you will be instrumental in shaping the user experience of our internal AI platforms. Your key responsibilities will include :
• Define AI Product Strategy Through UX Research : Lead end-to-end user research to directly inform the product roadmap for our agents and LLM-based applications. Identify critical pain points in scientific workflows and uncover new opportunities for AI to drive efficiency and discovery.
• Design and Execute Rigorous Usability Studies for AI Products : Develop and implement specialized user testing methodologies (e.g., contextual inquiries, task-based usability tests) to evaluate the effectiveness and intuitiveness of our LLM-driven interfaces and agentic workflows.
• Operationalize User Feedback for AI Systems : Build and manage a systematic process for gathering, analyzing, and tracking qualitative feedback on our models and agents, enabling data-informed improvements to the core AI infrastructure.
• Uncover Actionable Insights for Agentic Frameworks : Analyze qualitative data from user sessions to understand how scientists interact with and trust AI outputs. Synthesize findings to reveal what drives user adoption and engagement with complex AI tools.
• Build a User-Testing Infrastructure for Internal AI Tools : Create and maintain reusable testing protocols and recruitment pipelines tailored for internal, expert users, elevating the quality and efficiency of user research across the organization.
• Collaborate Deeply with AI / ML Engineers & Scientists : Work closely with technical teams to translate complex user needs from drug discovery units into clear, actionable product requirements for our AI models and agentic systems.
• Communicate with Clarity and Impact : Synthesize diverse data to provide compelling insights and recommendations. Effectively advocate for the user to both technical and non-technical stakeholders, influencing product features and the strategic roadmap.

Qualifications

Highly Preferred

Pay Rate Range : $75-92 / hr depending on experience

Equal Opportunity Employer : We are proud to be an equal opportunity employer. We welcome and encourage applications from all qualified candidates regardless of race, sex, gender identity or expression, disability, age, religion or belief, sexual orientation, or any other characteristic protected by applicable laws and regulations. It is our policy not to discriminate against any applicant or employee, and we are committed to fostering a diverse, inclusive, and respectful work environment across all locations in which we operate. We believe that diversity, equity, and inclusion are fundamental to our mission and enhance our ability to serve clients globally. If you have a disability or require any reasonable accommodations during the application or interview process, please inform your recruiter or contact us directly so that we can explore the appropriate arrangements.

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