Top Engineering Jobs in New York City, NY

Design and review data center white space infrastructure for high-density computing environments. Coordinate electrical, mechanical, cooling, low-voltage, cabling, and network pathway systems; review drawings, submittals, RFIs, and field conditions; oversee consultants; resolve design and constructability issues; and support construction, commissioning, and deployment. Contribute to design standards, scalability, technical documentation, and mentorship across CoreWeave’s data center portfolio.

Build and operate full-stack applications and AI-facing features for CoreWeave’s internal data platform. Responsibilities include developing TypeScript, React, Next.js, and Python services; designing APIs and relational data models; integrating analytical data platforms; deploying on Kubernetes; and owning production reliability, testing, observability, and on-call support. The role also involves collaborating with non-engineers to define processes, shipping LLM-backed applications, and evaluating AI output quality.

Build and ship full-stack software for CoreWeave’s internal data infrastructure and operational applications. Develop TypeScript, React, and Next.js frontends; Python services on Kubernetes; SQL queries and data models; and AI-enabled interfaces such as text-to-SQL tools, retrieval systems, and agents. Own scoped features through production, including testing and post-release fixes, while collaborating with senior engineers and internal users.

Leads systems engineering teams developing high-throughput file, block, and object storage for AI cloud infrastructure. Drives architecture, performance tuning, latency optimization, benchmarking, and 10x scaling across distributed storage systems. Provides technical mentorship, performance management, recruitment, and cross-functional leadership while maintaining a coding-focused culture and maximizing GPU utilization.

The role involves leading the Observability Insights initiative at CoreWeave, focusing on developing APIs, agentic tools, and improving telemetry for AI systems. Candidates should possess significant experience in backend engineering and observability systems, especially for multi-tenant environments.

Lead and develop an infrastructure engineering team responsible for internal Kubernetes platforms and foundational services. Set roadmaps, ownership, operating practices, and success metrics; guide reliable infrastructure and automation; establish testing, observability, SLO, incident response, change management, and on-call practices; resolve cross-team dependencies; and manage hiring, coaching, performance, priorities, and risk communication.

Design, develop, and maintain network datapath monitoring and observability infrastructure for GPU cloud services. Build real-time network telemetry and analytics pipelines across host networking, smart NICs, and overlay/underlay networks. Collaborate with DevOps, production, and data platform teams; troubleshoot Kubernetes and cloud networking issues; participate in on-call support, code reviews, architecture decisions, and continuous infrastructure improvements.

Design and implement distributed, exabyte-scale storage systems for AI workloads, including S3-compatible object storage and dedicated storage clusters. Improve storage reliability, durability, security, observability, throughput, latency, and resilience using technologies such as RDMA, GPU Direct Storage, and distributed filesystems. Monitor and troubleshoot production systems, develop performance dashboards, analyze telemetry, collaborate across infrastructure teams, and mentor engineers.

Design, build, and operate secure sandboxed runtime environments for Kubernetes and GPU-accelerated workloads. Develop GPU-aware scheduling, isolation, and resource management strategies; optimize container, VM, I/O, and GPU performance; conduct profiling and benchmarking; and contribute to Linux, container runtime, virtualization, and GPU driver architecture. Collaborate with security, platform, and infrastructure teams to establish runtime isolation and performance standards for multi-tenant systems.

Lead an engineering team responsible for CI, build systems, artifact management, and release automation. Set technical direction, modernize developer workflows, improve reliability and performance, track delivery metrics, manage technical debt, and drive adoption of platform capabilities. Mentor engineers through regular one-on-ones and career development while partnering with senior technical stakeholders. The role requires hands-on experience with scalable CI and build platforms, Kubernetes, cloud technologies, automation, and infrastructure as code.