Senior QA Analyst, AI Solutions

At Sword, we’re building AI to heal billions and unlock humanity’s full potential. In doing so, we’re pioneering AI Care, a fundamentally new approach to healthcare built for medical reasoning, safety, and real-time treatment, not generic technology applied after the fact. As both a clinical-centric frontier AI lab and an applied AI platform, Sword is reimagining how care is delivered at scale, removing traditional barriers like appointments, waiting rooms, and stigma so more people can access the care they need—and ultimately get back to lives lived in full.

Since 2020, Sword has expanded across physical therapy, women’s health, cardiometabolic, and mental health, and is now moving beyond the session to a fully AI-native, 24/7 care program that brings physical activity, therapeutic exercise, psychotherapy, nutrition, and behavior change into one connected experience. More than 700,000 members across three continents have completed over 10 million AI sessions, helping 1,000+ enterprise clients avoid more than $1 billion in unnecessary healthcare costs. Backed by 42 clinical studies, 44+ patents, and more than $500 million raised from leading investors including Khosla Ventures, General Catalyst, and Founders Fund, Sword is defining a new standard for healthcare.

AI Proficiency at Sword

AI fluency is a core expectation at Sword. Every candidate is assessed against our three-level framework — be ready to share real examples of how AI is already part of how you work.

Explorer (Level 1) — Uses AI daily to boost personal productivity

Builder (Level 2) — Creates workflows and tools that elevate the whole team

Integrator (Level 3) — Embeds AI into products and processes at scale

Every hire must demonstrate at least Level 1. The expected level will vary depending on the seniority of the role.

What you’ll be doing:

Ensure AI agents perform consistently before and after going live;

Monitor performance across different healthcare environments and languages;

Liaise with the clinical team to create protocols and understand what clinically relevant parameters should be monitored;

Establish quality standards that work for AI systems, not traditional software;

Fix the underlying prompt logic and reasoning chains;

Create new testing methods to prevent similar issues;

Figure out how to test systems designed to be intelligent and adaptive;

Build frameworks for testing across infinite healthcare scenarios;

Define what "good enough" means for AI in healthcare;

Manage compliance with MDR (Medical Device Regulation) and AI Act requirements for AI systems together with the compliance team;

Document quality processes and verification and validation procedures for regulatory submissions;

Help ensure the algorithms perform with the expected accuracy and do not lead to bias;

Work with compliance teams to achieve and maintain certification of the relevant algorithms;

Help establish industry standards for AI quality assurance;

Become one of the few experts in AI healthcare testing methodologies;