
We’re building Sophie, a multi-agent AI orchestrator that helps wealth management advisors deliver more personalized, effective service to their clients.
Our platform analyzes behavioral patterns, communication preferences, and emotional states to transform how advisors understand and serve their clients.
We’re a small, well-funded team at an exciting inflection point—our technology works, customers love the product, and now we’re building the engineering team to scale.
We’re looking for an Application QA Engineer to own quality across Sophie’s advisor-facing product. You’ll be our first dedicated QA hire, responsible for building our test automation framework, establishing quality processes, and ensuring that every feature advisors rely on—during live client calls—works flawlessly.
This is a hands-on, high-ownership role. You’ll define how we test, what we automate, and what “ready to ship” means. You’ll work closely with our frontend and backend engineers to catch issues before they reach production, and you’ll be instrumental in taking Sophie from beta to general availability.
Build and maintain our E2E test automation framework using Playwright
Create and expand regression test suites covering critical advisor workflows—login, client views, conversation flows, insights display, and recommendation panels
Implement visual regression testing to catch UI inconsistencies across browsers and devices
Set up component-level testing with React Testing Library + Vitest for frontend isolation
Integrate automated test suites into CI/CD pipelines (GitHub Actions) — tests run on every PR, nightly full regressions, and post-deployment smoke tests
Set up and maintain error tracking and monitoring (Sentry) to catch production issues proactively
Build test coverage dashboards so the team has visibility into what’s tested and what’s not
Own the release testing checklist—define what must pass before any deployment goes to production
Create a bug taxonomy and triage process—severity levels, SLAs, reproduction templates
Manage bug tracking workflows across Linear and Notion
Define the QA sign-off process—no feature ships without QA approval on acceptance criteria
Every confirmed bug gets an automated regression test so it never recurs
Audit the app end-to-end—document every user flow, edge case, and known issue
Perform cross-browser and cross-device testing—advisors use Sophie on different setups during client meetings
Establish performance baselines—load times, streaming response rendering, interaction responsiveness
Build and own the pre-launch quality gate for our production release
3+ years of QA experience with at least 2 years in test automation (not manual-only)
Proficiency with Playwright or Cypress—writing tests from scratch, not just recording them
Comfortable with TypeScript and testing within a React/Next.js codebase
Experience integrating test suites into CI/CD pipelines (GitHub Actions, GitLab CI, or similar)
Strong understanding of API contract testing—validating frontend/backend integration points
Solid bug documentation skills—clear reproduction steps, environment details, severity classification
Experience with visual regression testing tools (Percy, Chromatic, or Playwright screenshots)
Component testing experience with React Testing Library or similar
Familiarity with error tracking platforms (Sentry, Datadog, or similar)
B2B SaaS background, especially in professional services or financial contexts
Experience testing streaming/real-time UI (chat interfaces, live dashboards, collaborative tools)
Exposure to performance testing or load testing fundamentals
Self-directed and proactive—you find bugs before anyone reports them
Strong written communication—async-first remote culture requires clarity
Pragmatic about coverage—you know what to automate and what to test manually
Collaborative mindset—QA doesn’t exist in isolation, you work alongside engineers daily
Opinionated about quality—you’ll propose tools and processes, not wait to be told
We want to be upfront about expectations:
Not a manual-only QA role—automation is the core of this position, not a nice-to-have
Not a DevOps role—you’ll configure test pipelines, not manage infrastructure
Not an AI/ML testing role—a separate role handles LLM evaluation and data pipeline QA
Not a management role—near-term focus is individual contribution and building the QA foundation
Not passive—we need someone who drives quality standards, not someone who waits for tickets
Equity: Meaningful early-stage grant with 4-year vesting
Equipment: Professional Laptop ready to work with AI provided + stipend for remote work when 6 month mark is met
Time Off: Flexible PTO with a minimum 15 days encouraged
Learning: $1,000 annual professional development budget
Schedule: Flexible hours with 3-4 hours daily overlap (Americas timezones)