Principal Software Engineer

Skills

About the Role

You will lead the design, evolution, and reliability of a global financial data extraction and normalization platform. This system ingests filings across formats such as HTML, PDF, APIs, and 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. You will own the self-sourcing and extraction foundation used by multiple teams across the organization, and your mission will be 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 systems are built for reliability.

Requirements

• 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 distributed systems, data platforms and pipelines, AI/ML-powered extraction or classification systems, or platform/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
• Experience with document understanding, NLP, or financial data extraction (nice to have)
• Experience building provenance, lineage, or confidence-scoring systems (nice to have)
• Familiarity with cloud-native architectures and modern data stacks (nice to have)
• Experience shaping or owning internal platforms used by multiple teams (nice to have)

Responsibilities

• Own the architecture and evolution of the large-scale data extraction and normalization platform
• Design systems that process hundreds of thousands of records across heterogeneous sources 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
• Balance AI-driven approaches with procedural and rules-based systems to improve reliability and explainability
• Identify 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

Benefits

• Equity
• Generous benefits program