Top Tech Jobs & Startup Jobs in NYC, NY

Executes daily fund operations across capital calls, distributions, wires, reconciliations, expenses, credit facilities, investor records, and quarterly financial reviews. Partners with fund administrators, banks, auditors, and internal teams to ensure accurate, timely execution. Documents workflows and builds automation for payment matching, expense allocation, accruals, reconciliations, reporting, and exception alerting, helping the platform scale its fund portfolio without proportional headcount growth.

Owns end-to-end fund accounting and administration for private market funds, including financial statements, capital calls, distributions, investor allocations, cash management, and audit support. Interprets fund documents, partners with banking, legal, audit, and tax providers, resolves investor inquiries, and collaborates with product and engineering teams to automate workflows and improve compliance processes.

Own end-to-end accounting and operations for complex private equity and venture capital funds, including allocations, capital calls, distributions, investor reporting, cash management, financial statements, and audit support. Review accountants’ work, interpret partnership agreements and side letters, calculate waterfalls and fees, coordinate with external providers, mentor colleagues, and partner with product and engineering teams on automation and process improvements.

Lead as a technical operator between product, engineering, design, and leadership to remove blockers, accelerate delivery, and compress feedback loops. Drive rapid prototyping-to-production cycles, introduce AI tooling and lightweight processes, directly contribute to delivery, surface risks and dependencies, and scale team workflows so the product organization ships higher-quality features faster.

Design and implement backend APIs and performant database schemas (Postgres/SQL); build scalable, testable C# services; work in Agile cross-functional squads; incorporate AI-assisted development tools; and drive engineering maturity and efficiency.

Build, train, fine-tune, evaluate, and deploy machine learning and LLM models for production. Create monitoring, evaluation, and feedback tooling, evaluate new techniques, and collaborate cross-functionally to ship ML-powered features.