Top Tech Jobs & Startup Jobs in NYC, NY

Own Nebius’s enterprise product marketing narrative, positioning, messaging, customer journey, and go-to-market programs for AI cloud infrastructure. Develop enterprise content, competitive positioning, sales and partner enablement, ROI/TCO tools, and account-based programs supporting lengthy buying cycles. Translate complex AI infrastructure concepts for technical and executive audiences, measure pipeline and win-rate impact, support communications and analyst relations, and bring customer insights into product and messaging strategy.

Own GTM reporting and analytics across Salesforce, warehouse systems, Tableau, and Excel. Build trusted dashboards, define metrics, investigate data quality issues, and lead analysis for pipeline, forecasting, territory design, capacity planning, productivity, and resource allocation. Partner with GTM, CRM, Data, and Systems stakeholders to improve reporting architecture and support leadership decisions. Use AI tools to automate analysis and develop internal analytics workflows.

Serve as an embedded risk and controls partner for Tax, Treasury, HR and corporate processes. Lead risk assessments, maintain RCMs and process documentation, design and implement practical controls, coordinate SOX and audit activities, manage remediation, drive automation and monitoring, and support transformations and system implementations to improve control effectiveness and reporting.

The Senior Network Engineer will design, manage, and optimize large-scale data center networks for AI workloads, ensuring reliability and performance.

Collaborate on mechanical designs for data center systems, validating performance, overseeing construction, and ensuring designs meet quality benchmarks.

The Cloud Solutions Architect will design and implement cloud solutions for AI and ML, optimizing client pipelines and providing technical guidance.

As a System Engineer, you'll design, deploy, and maintain cloud systems for AI workloads, troubleshoot complex issues, and collaborate with teams to optimize performance.

Design, build, and maintain backend systems for metrics and monitoring, collaborate across teams, and debug production incidents.

Serves as the primary technical partner for customers deploying and operating GPU clusters and AI infrastructure. Responsibilities include configuring and tuning GPU environments, troubleshooting hardware, networking, operating system, and cluster issues, coordinating with internal engineering teams, translating requirements into architectures and execution plans, documenting solutions, and identifying performance and reliability improvements.

Lead mechanical engineering evaluations for data center opportunities, assessing HVAC, cooling, chiller, CRAH/CRAC, liquid cooling, and critical equipment systems. Conduct facility due diligence, validate capacity and design assumptions, evaluate vendors and deployment risks, and provide feasibility and Go/No-Go recommendations. Support mechanical design strategies, equipment selection, commissioning, and construction readiness while partnering with engineering teams, consultants, and vendors to establish scalable standards for hyperscale and AI infrastructure.