Top Tech Jobs & Startup Jobs in NYC, NY

Build and operate backend services, control planes, and automation for Lightning AI’s managed Kubernetes and Slurm infrastructure. Develop distributed systems for cluster provisioning, lifecycle management, scheduling, observability, reliability, and scalability across large-scale GPU environments. Diagnose complex production issues and collaborate with infrastructure, AI, and platform engineering teams. Contribute to architecture, technical design, mentoring, engineering practices, and on-call operations.

Designs, deploys, automates, and operates large-scale NVIDIA InfiniBand fabrics for GPU clusters. Manages UFM, switches, firmware, congestion control, routing, QoS, observability, troubleshooting, capacity planning, and incident response. Collaborates with AI platform, GPU infrastructure, storage, and systems teams to build reliable AI Factory environments.

Design end-to-end user experiences for AI and machine learning products, collaborating with product and engineering teams from strategy through release. Create user journeys, interaction designs, prototypes, and visual designs; conduct user research; engage with open-source AI and enterprise users; identify opportunities to reduce friction; and advocate for scalable design systems. This hybrid role requires strong product, interaction, and visual design skills in a fast-paced startup environment.

Design, build, and maintain scalable backend services and APIs powering Lightning AI Studio. Develop platform capabilities for authentication, resource management, multi-tenancy, and developer workflows. Improve reliability, observability, scalability, and performance across cloud-native infrastructure. Collaborate with product, infrastructure, and AI engineering teams, lead technical initiatives from architecture through production, evaluate engineering processes, and mentor engineers.

Design and build backend systems for AI agent orchestration, tool execution, workflow management, memory, and state handling. Develop scalable APIs and reliable infrastructure for distributed AI workflows, improve platform reliability and observability, and partner with product, research, and engineering teams to deliver agent capabilities. Own technical initiatives from architecture through production, maintain software quality through testing and continuous delivery, and mentor engineers on backend and distributed-systems practices.

Partners with senior business leaders on organizational design, team structure, leveling, promotions, compensation, employee relations, performance management, and exits. Builds manager capability, improves hiring quality, and develops scalable people processes with Legal, Total Rewards, and Finance. Supports globally distributed teams and helps reduce organizational risk through practical, high-impact systems and policies.

Own the company’s data infrastructure, including ETL/ELT pipelines, clean analytical tables, data quality, schema management, freshness monitoring, lineage, and alerting. Partner with engineering, product, ML, data science, and go-to-market teams to improve instrumentation and deliver reliable product and operational insights. This high-scope role requires hands-on technical leadership, data governance, and scaling analytics capabilities in a rapidly changing environment.

Develop and scale Lightning AI’s backend platform using Go, spanning APIs, infrastructure, billing, security, and integrations. Own features end to end, design reliable and scalable systems, improve architecture and performance, maintain software quality and continuous delivery, reduce technical debt, and mentor engineers. Collaborate with engineering, product, and design teams in a fast-changing SaaS environment.

Build and deploy production AI systems for customers, translating business objectives into scalable technical solutions. Responsibilities include architecture, proof-of-concepts, software development, deployment, monitoring, debugging, inference optimization, and distributed systems operation. The engineer partners with customer engineering teams, collaborates with product and engineering, improves reusable platform capabilities, and owns technical engagements from discovery through production scaling.

Build and operate production software, APIs, tooling, and automation for large-scale GPU, bare-metal, and HPC infrastructure. Responsibilities include provisioning, configuration, monitoring, lifecycle management, observability, hardware integration, reliability improvements, and infrastructure capacity deployment. The role partners with networking, data center, platform, and infrastructure teams to design scalable systems and define technical direction.