Top Tech Jobs & Startup Jobs in NYC, NY

Leads multiple engineering teams delivering AI-powered data connectivity solutions. Owns execution, delivery, technical direction, operational reliability, and cross-team coordination. Develops engineering talent through coaching, hiring, and performance management while partnering with Product and Engineering leadership on priorities and organizational planning. Guides architecture, cloud-native development, incident response, SLOs, and AI-assisted software development practices. Requires strong Java/Kotlin expertise, cloud architecture experience, and technical judgment across distributed systems.

Lead end-to-end development of an AI-driven decision-support system for multi-sensor data. Responsibilities include building data ingestion and fusion pipelines, training and deploying supervised and unsupervised ML models, optimizing near-real-time inference, creating GPU-accelerated dashboards, integrating modular containerized systems, and supporting secure deployment and testing. The role works directly with the founder, owns technical delivery autonomously, and collaborates with subcontractors on human factors, model validation, and security auditing.

Manage and grow a product engineering team at a remote cloud cost-management software company. Lead prioritization, delivery, quality standards, recruiting, and cross-functional collaboration with product, design, and leadership. Help engineers ship customer-focused features spanning spend management, interfaces, data pipelines, visualizations, and application performance. Coach engineers, remove blockers, promote ownership, and guide tradeoffs between velocity and quality.

Design and build cloud cost-management capabilities, including forecasting, anomaly alerts, cost allocation, data pipelines, transformation models, visualizations, and reusable UI components. Own features end to end from planning through release while improving application reliability and performance. Collaborate closely with engineers and founding team members, contribute to technical design and roadmap decisions, and work pragmatically in a fast-moving distributed startup environment.

Build and maintain cloud infrastructure, automation, configuration management, and CI/CD pipelines. Operate Kubernetes, Helm, and Docker workloads across multiple public clouds; configure observability tooling; troubleshoot production systems spanning networking, databases, serverless functions, and caching; and participate in a 24/7 on-call rotation. The role also involves owning infrastructure projects end to end and collaborating across engineering and product teams.

Own and improve backend services for an AI-native learning platform: build Python/Node.js services, design serverless/cloud-native architectures, implement event-driven workflows and advanced NoSQL models, integrate LLM-based features (RAG, embeddings, vector search), ensure production reliability, observability, testing, and frontend API integrations while collaborating in a cross-functional remote team.

Design, build, and operate scalable backend systems and APIs across a distributed service-oriented architecture. Own features end-to-end, partner with product stakeholders, ship shared infrastructure, and maintain high code quality. Participate in an on-call rotation and collaborate in a distributed North American team to deliver reliable, high-performance user experiences.

Build and maintain platform systems that provision, secure, and observe isolated agent execution environments. Design reusable primitives for queues, jobs, locks, and service-bus mechanics. Extend Pulumi-based IaC, work with containers, Linux internals, storage, permissions, logging, and runtime debugging. Ship reliable, maintainable infrastructure-driven application code and collaborate in a small cross-functional team.

Design, build, and maintain scalable backend services and APIs; deliver product features end-to-end; maintain integrations; diagnose and resolve performance and reliability issues; improve engineering practices and architecture; support large-scale data ingestion and unified data access layers.

Lead end-to-end delivery of full-stack product and platform initiatives. Design and implement scalable frontend and backend systems, own production reliability and incidents, introduce pragmatic engineering practices, mentor engineers, and drive architecture and tooling decisions to balance speed and long-term maintainability.