Top Tech Jobs & Startup Jobs in NYC, NY

Develop kernel-centric reliability solutions for AI compute clusters and production services. Responsibilities include debugging failures, building diagnostic tools, supporting incident response, performing root-cause analysis, improving kernel and software reliability, and collaborating with systems, hardware, ASIC, and architecture teams on reliability-focused designs.

Lead architecture and evolution of enterprise, data center, and cloud networks for hyperscale AI; design secure, high-performance fabrics (spine/leaf, RDMA); build AI-agent review frameworks; implement segmented zero-trust network designs; set resiliency, observability, and capacity standards; mentor global engineers and guide cross-functional initiatives.

Design, build, and operate firewall, segmentation, and zero-trust controls across data center, corporate, and cloud networks. Implement infrastructure-as-code for policy and CI/CD deployment, manage lifecycle and rule hygiene, build network detection capabilities, operate VPN/remote access patterns, and document architecture and runbooks while partnering with Network Engineering and Security Operations.

Join IT & Security to secure and scale enterprise IT, cloud, network, and infrastructure; build automation and tooling; support detection, response, and vulnerability management; improve identity, endpoint, and systems security; and partner cross-functionally to develop processes that enable secure, reliable, and scalable AI workloads.

Operate and scale production AI inference infrastructure, run releases and capacity changes, build self-service CD pipelines and automation, extend telemetry and observability, collaborate on SLOs, post-mortems, and capacity planning to reduce operational toil.

Design and implement system-level debugging, validation, and observability platforms. Build automated anomaly collection/analysis, visualization and root-cause tools, failure classification and monitoring frameworks. Extend compilers, runtimes and programming interfaces for profiling and instrumentation, improve bring-up and low-level debug workflows, lead cross-functional initiatives, support incident response, and establish debuggability and reliability best practices.

Develop, optimize, and validate high-performance ML and linear-algebra kernels for Cerebras wafer-scale hardware. Implement low-level assembly and a C-like DSL (CSL), model and analyze performance, build testing methodologies, and collaborate with chip and system architects to maximize compute utilization for state-of-the-art AI and HPC workloads.

Design, implement, optimize, and validate high-performance ML and linear algebra kernels for Cerebras hardware. Develop low-level assembly and CSL routines, use mathematical performance models, create unit/system tests, and collaborate with chip and system architects to maximize compute utilization and scale kernels for state-of-the-art AI/HPC workloads.

Design and implement high-performance distributed runtime components for large-scale training and inference. Optimize data and communication pipelines, enable scalable multi-node execution, collaborate with ML and compiler teams, diagnose performance issues via profiling, and contribute to system architecture and roadmap for cutting-edge AI workloads.

Integrate, validate, and productionize cross-stack inference features across AI frameworks, runtime, compiler, kernels, distributed systems, and hardware. Drive zero-to-one projects, debug system-wide failures, manage accelerated timelines, and improve automation, diagnostics, and repeatable integration practices while collaborating across software and hardware teams.