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Principal Software Engineer

AlphaSenseNew York City, NY

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Overview

Schedule
Full-time
Career level
Senior-level
Benefits
Career Development

Job Description

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 impacting scale, accuracy, and developer velocity
  • Act as the technical authority to block poor designs, redesign critical systems, and introduce new platforms or tooling when necessary
  • Partner with product and downstream consumers to define quality bars, SLAs, and success metrics
  • Serve as a technical escalation point for production issues, reliability failures, and systemic risks
  • Mentor senior engineers, shape technical culture, and raise expectations for system design and execution
  • Contribute to long-term technical strategy while remaining hands-on with critical implementations

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