Top Tech Jobs & Startup Jobs in NYC, NY

Supports day-to-day business operations through record-keeping, invoice and reconciliation workflows, contract and compliance documentation, reporting, tool administration, shared inbox monitoring, cross-team coordination, and process improvement projects. The role is designed for an early-career professional with strong organization, attention to detail, communication skills, and an interest in finance and operational processes.

Build and operate large-scale data pipelines, web-scraping systems, and data-collection tools for AI datasets. Maintain analytical databases, optimize queries, troubleshoot pipeline and data-quality issues, manage containerized workloads across Kubernetes and Linux infrastructure, and support CI/CD workflows. The role also involves documenting technical processes, researching improvements, and developing scalable APIs in a fully remote environment.

Review applications and product changes before release to ensure compliance and quality. Validate and document findings, provide actionable feedback to engineering and operations, verify fixes, maintain review documentation, and improve internal review processes to ensure consistent, high-quality releases.

Build and maintain backend systems in TypeScript, improve observability and metrics, automate deployments and Slack reporting, test and troubleshoot production systems, and clean up the existing codebase.

Design, build, and optimize scalable real-time and batch data pipeline infrastructure. Develop backend features focused on reliability and performance, write well-tested code, review technical designs, and contribute to infrastructure across mobile, desktop, and server systems.

Design, build, and operate large-scale distributed web crawlers and data pipelines. Handle anti-bot systems and dynamic/JS-heavy sites, perform cleaning, deduplication, filtering, and normalization, and maintain datasets for model training while optimizing for cost, latency, and reliability.

Develop, fine-tune, and deploy LLMs for NLP tasks; design and maintain large-scale data pipelines; analyze time-series data; implement data-driven models and OCR systems; ensure data quality; collaborate cross-functionally; and research and apply best practices to improve internal data processing tools and infrastructure.