Principal Software Engineer

About AlphaSense: The world’s most sophisticated companies rely on AlphaSense to remove uncertainty from decision-making. With market intelligence and search built on proven AI, AlphaSense delivers insights that matter from content you can trust. Our universe of public and private content includes equity research, company filings, event transcripts, expert calls, news, trade journals, and clients’ own research content. The acquisition of Tegus by AlphaSense in 2024 advances our shared mission to empower professionals to make smarter decisions through AI-driven market intelligence. Together, AlphaSense and Tegus will accelerate growth, innovation, and content expansion, with complementary product and content capabilities that enable users to unearth even more comprehensive insights from thousands of content sets. Our platform is trusted by over 6,000 enterprise customers, including a majority of the S&P 500. Founded in 2011, AlphaSense is headquartered in New York City with more than 2,000 employees across the globe and offices in the U.S., U.K., Finland, India, Singapore, Canada, and Ireland. Come join us! About the Team: Our diverse Product & Engineering team values innovation, collaboration, and the continuous improvement of our platform. We offer a dynamic work environment where employees can grow and thrive while working on cutting-edge technology that shapes the future of AI and search. At AlphaSense, we believe that diverse experiences and perspectives are critical to our success. We are committed to creating an inclusive workplace where all employees feel valued and empowered to be their authentic selves. About the Role: We are seeking a Principal Engineer to lead the design, evolution, and reliability of our global financial data extraction and normalization platform. This system ingests filings across formats (HTML, PDF, APIs, spreadsheets), extracts and standardizes financial data using a combination of AI and deterministic systems, and delivers trusted, traceable outputs at scale to downstream financial services teams and customers. This role owns the self-sourcing and extraction foundation used by multiple teams across the organization. Today, human validation is used as a safety net. Your mission is to systematically reduce human dependency by increasing system correctness, confidence, and trust, without sacrificing speed or scalability. You will operate at the intersection of distributed systems, AI-driven extraction, data quality, and platform architecture, raising the technical bar and reshaping how we build reliable systems. Who You Are: 15+ years of experience building and operating large-scale production systems Proven experience designing data ingestion, extraction, or processing systems at scale Deep expertise in one or more of: Distributed systems Data platforms and pipelines AI/ML-powered extraction or classification systems Platform or infrastructure engineering Demonstrated ability to design trustworthy systems that combine probabilistic (AI) and deterministic approaches Strong understanding of system reliability, observability, failure modes, and iterative hardening Experience reducing operational or human overhead through better system design Track record of technical leadership across teams without formal people management Comfortable operating in ambiguity and driving clarity where none exists Passion for using AI responsibly and effectively to deliver scalable, high-confidence systems Nice to Have Experience with document understanding, NLP, or financial data extraction Experience building provenance, lineage, or confidence-scoring systems Familiarity with cloud-native architectures and modern data stacks Experience shaping or owning internal platforms used by multiple teams What You’ll Do: Own the architecture and evolution of our large-scale data extraction and normalization platform Design systems that process hundreds of thousands of records across heterogeneous sources (PDF, HTML, APIs, XLS) with high reliability Define and implement strategies to minimize human-in-the-loop validation through AI-based validation, confidence scoring, provenance tracking, and deterministic safeguards Establish clear system contracts for correctness, traceability, and confidence (e.g., “where did this number come from?”) Balance AI-driven approaches with procedural and rules-based systems where they improve reliability and explainabilityIdentify and remediate architectural and operational bottlenecks i