Senior Product Manager, AI Infrastructure
About this role
Delivering reliable access to large-scale GPU compute remains a primary challenge for machine learning teams moving from experimentation to production. This role addresses that problem by leading the product strategy for Lightning AI's GPU cloud infrastructure, ensuring researchers and developers can efficiently provision and manage compute resources. Operating at the intersection of developer experience and cloud platforms, the successful candidate will define how users discover and consume infrastructure while collaborating directly with engineering and operations teams to optimize cluster performance.
Reporting to the VP of Product, this position involves close partnership across engineering, sales, and executive leadership to shape pricing, packaging, and platform capabilities. The right person for this role combines deep technical literacy in distributed systems with a strong ownership mindset, navigating ambiguity to deliver scalable infrastructure solutions.
What you will do
- Define and drive the product vision, roadmap, and execution for the GPU cloud infrastructure platform.
- Partner with infrastructure and platform engineering to improve availability, reliability, utilization, and performance.
- Translate customer workloads across experimentation, training, and inference into clear infrastructure capabilities.
- Establish pricing, packaging, and consumption models alongside operations, sales, and finance teams.
- Determine the APIs, interfaces, and developer workflows that enable customers to interact with infrastructure.
- Track metrics related to GPU utilization, provisioning, workload reliability, and customer adoption.
What you need
- Bring seven or more years of product management experience, including at least three years focused on cloud infrastructure, compute, platform, developer tooling, or AI infrastructure.
- Demonstrate a strong understanding of core cloud infrastructure concepts such as compute, networking, storage, provisioning, scheduling, and orchestration.
- Possess familiarity with GPU infrastructure and the operational requirements of large-scale AI training, experimentation, or inference workloads.
- Maintain technical depth sufficient to collaborate directly with engineers on APIs, distributed systems, Kubernetes, workload orchestration, observability, and infrastructure reliability.
- Utilize data effectively to analyze infrastructure utilization, capacity, reliability, performance, and customer behavior.
- Hold a Bachelor of Science in Computer Science, Engineering, or possess equivalent practical experience.
Nice to have
- Experience at a GPU cloud, neocloud, hyperscaler, AI infrastructure company, or infrastructure developer-tools company.
- Experience building products involving GPU provisioning, cluster management, capacity management, workload scheduling, or distributed compute.
- Familiarity with GPUs, Kubernetes, Slurm, Ray, PyTorch, distributed training, or similar infrastructure technologies.
- Experience with reserved capacity, on-demand compute, utilization optimization, or other cloud consumption models.
- Experience working closely with data center, hardware, networking, or infrastructure operations teams.
Benefits
- Comprehensive health coverage including medical, dental, and vision for employees and eligible dependents
- Meaningful equity through RSUs
- Retirement savings via 401(k) matching in the U.S. and pension contributions in the U.K.
- Flexible time
Skills and tech stack
Questions candidates ask
What is the remote work policy for this position?
This is a hybrid role based in New York City or San Francisco, with an in-office expectation of two days per week.
What is the expected salary range for this role?
The anticipated annual base salary range for this role is $200,000 to $250,000 USD. Final offers are based on factors such as experience, skills, geographic location, and role expectations.
What additional compensation is included beyond base salary?
In addition to base salary, the total rewards package includes a discretionary bonus, a meaningful equity component with RSUs, and comprehensive benefits.