← All jobs · Crusoe

Senior Technical Program Manager, Networking

Crusoe ·
47
AI-Agency
B45 U50
📍 San Francisco, US 💰 $161K–$196K 🛠 AI tools welcome at work Senior 3–6+ yrs
KubernetesVPC networkingNVIDIA networking platforms
TL;DR

Senior Technical Program Manager at Crusoe managing networking feature delivery for IaaS infrastructure products. Owns end-to-end execution of networking features across product, engineering, and data center operations teams.

Apply at Crusoe →
share:
you'll be redirected to the company's career page

Job description

Crusoe is on a mission to accelerate the abundance of energy and intelligence. As the only vertically integrated AI infrastructure company built from the ground up, we own and operate each layer of the stack — from electrons to tokens — to power the world's most ambitious AI workloads. When you join Crusoe, you join a team that is building the future, faster.

We're in the midst of the greatest industrial revolution of our time. The demand for AI compute is boundless, and power is a bottleneck. We're solving that — with an energy-first approach that makes AI infrastructure better for the world and faster for the people innovating with AI.

We're looking for problem-solving, opportunity-finding teammates with a sense of urgency, who believe in the scale of our ambition and thrive on a path not fully paved — people who want to grow their careers alongside a team of experts across energy, manufacturing, data center construction, and cloud services.

If you want to do the most meaningful work of your career, help our customers and partners advance their AI strategies, and be part of a high-performing team that believes in each other, come build with us at Crusoe.

About This Role

We're looking for a Senior Technical Program Manager to own the delivery of networking feature sets within our IaaS product portfolio. This is a hands-on IC role sitting at the intersection of Product, Engineering, and Data Center Operations.

Networking is one of the highest-leverage surfaces in the IaaS product. When a networking feature is late or poorly scoped, every dependent team feels it. You will own the execution of defined networking feature sets from backlog to GA, keeping engineering unblocked and delivery predictable across interdependent workstreams.

You don't need to be a network engineer. You do need to understand how data center networks are built, how networking capabilities are exposed as IaaS products, and how to keep engineering teams moving when things get complicated.

What You'll Own

Networking Feature Delivery: Drive end-to-end delivery of networking feature sets within IaaS products such as Virtual Machine networking, Kubernetes networking, and cloud foundations work. Translate engineering backlogs into structured timelines, surface blockers early, and own status communication across Product, Engineering, and DC Ops.

Backlog and Dependency Health: Work alongside engineering leads and product managers to maintain backlog health, track dependencies between networking and adjacent workstreams including Compute, Storage, Firmware, and Cloud Foundations, and ensure teams are unblocked before reviews, not during them.

Scoping and Readiness: Engage early in the feature lifecycle to help define acceptance criteria, readiness requirements, and Go/No-Go criteria before development begins. The earlier you're in, the more you can shape.

Program Infrastructure: Build lightweight execution structures including standups, dashboards, dependency trackers, and status updates that engineering teams actually adopt. Establish predictability without adding unnecessary process weight.

Cross-Functional Coordination: Coordinate across Product, Engineering, DC Ops, and external hardware partners on networking programs with external dependencies. Surface ownership gaps and drive them to resolution.

What You'll Bring

Data Center Networking Fluency: You understand how data center networks are built and how networking capabilities are delivered as IaaS products. Frontend and backend fabric, VPC networking, and how networking decisions interact with compute and storage layers should be part of your vocabulary. You don't need to design the network yourself, but you need to engage credibly when engineers.

IaaS Product Context: Familiarity with how networking features are built, scoped, and shipped in a cloud infrastructure environment. Understanding of how product requirements flow into engineering workstreams and what causes feature delivery to slip.

Build-Side Experience: 3 to 6 years as a Technical Program Manager in a data center infrastructure, networking, or IaaS delivery context. Experience at a hyperscaler (AWS, Azure, Google, Meta, OCI), neocloud (CoreWeave, Lambda Labs), or infrastructure-focused company. You've been on the build side shipping infrastructure products, not supporting them post-deployment.

Execution in Ambiguity: Comfortable building structure where little exists. You make programs predictable without making them bureaucratic.

Clear Communication: You can write a crisp status update, run a tight standup, and give a senior stakeholder an honest read on program risk without burying it in caveats.

Nice to Have:

Experience owning feature delivery within an IaaS product team. Familiarity with how networking features are tested and validated before GA. Exposure to NVIDIA networking platforms or similar at-scale fabrics. Active use of AI tools to improve how you work.

Bonus Points:

Benefits:

Compensation Range:

Compensation will be paid in the range of $161,700 to $196,000 plus Bonus. Restricted Stock Units are included in all offers. Compensation to be determined by the applicant’s education, experience, knowledge, skills, and abilities, as well as internal equity and alignment with market data.

Crusoe is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, disability, genetic information, pregnancy, citizenship, marital status, sex/gender, sexual preference/ orientation, gender identity, age, veteran status, national origin, or any other status protected by law or regulation.

Apply at Crusoe →

More open roles at Crusoe

Crusoe · 🔄 synced 59m ago
Senior Director, AI Model Lifecycle
📍 San Francisco, US 💰 $301K–$355K · Director
Senior Director leading Crusoe's AI Model Lifecycle team, responsible for building fine-tuning systems, training pipelines, and managed platforms for large language models. Oversees team of ML engineers and manages end-to-end LLM training infrastructure including SFT, PEFT, LoRA, and reinforcement learning workflows.
PyTorchGolangPythonvLLMGPU systems
89
AI-core
Crusoe · 🔄 synced 59m ago
Staff Enterprise AI Automation Engineer
📍 San Francisco, US 💰 $190K–$230K 🛠 AI tools welcome at work · Staff
Staff Enterprise AI Automation Engineer at Crusoe designing and building agentic AI systems that orchestrate workflows across enterprise platforms. Focus on LLM integration, agent architecture, and scalable automation infrastructure.
PythonRESTGraphQLWorkatoAnthropic ClaudeGoogle Gemini
84
AI-core
Crusoe · 🔄 synced 59m ago
Staff Product Manager, Managed Intelligence (SF/Sunnyvale)
📍 San Francisco, US 💰 $204K–$247K 🛠 AI tools welcome at work · Staff
Staff Product Manager at Crusoe leading product strategy for Managed Intelligence services. Focus on defining AI and agentic capabilities, model lifecycle, and scaling cloud products for AI-native companies.
PyTorchJAXTensorFlowKubernetesAWS
76
AI-core
Crusoe · 🔄 synced 59m ago
Senior Staff Software Engineer, AI Model Lifecycle
📍 San Francisco, US 💰 $237K–$318K · Staff
Senior Staff Software Engineer at Crusoe building managed platforms for AI model lifecycle. Focus on fine-tuning systems, training pipelines, reinforcement learning, and dataset management for large language models at scale.
PyTorchGolangPythonvLLMGPU systems
73
AI-fluent
Crusoe · 🔄 synced 59m ago
Staff Software Engineer, AI Model Lifecycle
📍 San Francisco, US 💰 $208K–$279K · Staff
Staff Software Engineer at Crusoe building managed platforms for AI model lifecycle. Focus on fine-tuning systems, training pipelines, reinforcement learning, and dataset management for large language models at scale.
PyTorchGolangPythonvLLMGPU systems
73
AI-fluent