QE

About AlphaSense: 

AlphaSense is the AI platform redefining market intelligence and workflow orchestration, trusted by thousands of leading organizations to drive faster, more confident decisions in business and finance. The platform combines domain-specific AI with a vast content universe of over 500 million premium business documents — including equity research, earnings calls, expert interviews, filings, news, and internal proprietary content. Purpose-built for speed, accuracy, and enterprise-grade security, AlphaSense helps teams extract critical insights, uncover market-moving trends, and automate complex workflows with high-quality outputs. With AI solutions like Generative Search, Generative Grid, and Deep Research, AlphaSense delivers the clarity and depth professionals need to navigate complexity and obtain accurate, real-time information quickly. For more information, visit www.alpha-sense.com. Come join us.

About the team

Every insight on AlphaSense is tied to a company. Reference Entity Data Services (REDS) owns the data that makes that possible: one trusted record for every company, connected across all the sources we bring in.

REDS turns records from many providers into one authoritative view, keeps it current, and supplies it to every part of the platform that depends on it, from search to company profiles. When that foundation is right, customers find the right company and trust what they see. 

About the Role

As the Staff Quality Engineer dedicated to REDS, you will define how we prove our data is correct before it reaches customers. You will set the strategy, build the practice from the ground up, and work closely with engineering and product leadership to make quality a measurable part of how the team ships.

What You'll Do

• Own the quality strategy for AlphaSense's company reference data, built on quality by design and automation by default.
• Close test coverage gaps on our critical workflows: ingesting provider data, matching records, keeping identifiers stable over time, and delivering entity data and change events to the product teams that rely on them.
• Define what "correct" means for company data, and build the golden datasets, labeled samples, and automated checks that prove it, including measuring match precision and recall and the tradeoff between false merges and false splits.
• Set the quality measures and release standards the team uses to ship with confidence.
• Build and grow scalable automation across our data, API, and event-driven interfaces, and decide what belongs at the unit, component, API, and E2E layers.