Top Tech Jobs & Startup Jobs

Operate and scale large GPU infrastructure platforms, including Linux systems, bare-metal environments, provisioning workflows, observability, and reliability automation. Responsibilities include platform deployment, incident response, break/fix operations, customer provisioning, on-call participation, infrastructure troubleshooting, and collaboration across engineering, networking, customer success, and software teams. The role also develops automation to reduce manual work and improve operational efficiency.

Own compensation philosophy, incentive and equity design, compensation planning cycles, market benchmarking, offer and leveling frameworks, and analytical models. Build compensation and equity tools, dashboards, and reporting for pay equity, offer competitiveness, retention risk, and performance cycles. Partner cross-functionally with People, Finance, and senior leaders to translate market and modeling insights into compensation decisions.

Own cross-functional operations projects that remove growth blockers, diagnose organizational friction, and build scalable AI-powered internal tools and automations. Partner with teams across the company, drive project adoption, maintain internal operations documentation, support business programs, and measure outcomes using data. The role requires strong analytical thinking, stakeholder management, end-to-end project ownership, and hands-on experience building workflows with APIs, SaaS platforms, automation tools, and AI coding tools.

Lead and develop the customer success organization for technical, enterprise, and growth customers. Own adoption, retention, renewal readiness, customer health, expansion influence, and executive relationships. Build scalable onboarding, success planning, health scoring, forecasting, escalation, and expansion processes. Partner with Sales, Product, Engineering, Support, Marketing, and Revenue Operations to improve customer outcomes and product adoption while coaching teams and creating repeatable customer success playbooks.

Leads complex, cross-functional infrastructure programs spanning compute, networking, storage, observability, hardware, and datacenter operations. Owns program planning, milestones, dependencies, risks, and execution across multiple workstreams. Partners with engineering, operations, security, platform, and leadership teams to deliver network expansions, datacenter growth, hardware rollouts, GPU platform deployments, and infrastructure readiness. Provides leadership updates and improves execution frameworks, tooling, and program visibility.

Develop and post-train deep learning models while building systems, tooling, and workflows for training, evaluation, debugging, and deployment. Contribute to open-source projects, distributed AI infrastructure, backend services, and developer platforms. Collaborate with research, product, infrastructure, and customer teams to solve complex technical problems, prototype ideas, and productionize successful experiments.

Own the vision, roadmap, launch, adoption, pricing, and go-to-market for Lightning AI’s experimentation and post-training products. Define workflows for experiments, fine-tuning, reinforcement learning, evaluation, model comparison, and production handoffs. Collaborate closely with engineering, customers, executives, sales, Growth, and Finance. Drive product strategy, technical requirements, model evaluations, pricing, metrics, developer experience, and end-to-end execution for ML infrastructure and developer tooling.

Design and scale backend systems for AI agent orchestration, distributed workflows, experiment management, platform APIs, and developer tooling. Build reliable cloud-native infrastructure, improve observability and performance, partner across engineering and research teams, maintain software quality through testing and continuous delivery, and mentor engineers on distributed systems and backend best practices.

Develop and scale the Lightning AI platform across frontend, CLI, APIs, and backend systems. Build features using React, Python, or Go; improve stability, performance, architecture, automation, and continuous delivery. Collaborate with engineering, product, and design leaders, own end-to-end feature development, reduce technical debt, and mentor engineers on system design and problem-solving.

Support ML engineers running large-scale training and inference workloads across Kubernetes, cloud, and GPU infrastructure. Diagnose distributed PyTorch, CUDA, networking, storage, scheduling, performance, and reliability issues. Analyze observability data, guide customers through complex infrastructure problems, support production incidents, and improve platform reliability through automation, tooling, documentation, runbooks, and operational processes. The role partners closely with infrastructure, networking, and platform engineering teams in a hybrid office environment.