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About This Role
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 the Role:
The Staff Product Manager, Fleet Management owns the product strategy and execution for the systems that keep Crusoe's GPU and CPU fleet running reliably and efficiently at scale. You'll define how Crusoe provisions, monitors, maintains, and optimizes its server fleet supporting AI training and inference workloads, translating operational requirements into scalable product capabilities. This covers the full lifecycle of compute: initial provisioning, firmware and configuration management, burn-in, multi-node testing, health monitoring, maintenance orchestration, repair, and decommissioning.
The role sits at the intersection of infrastructure operations, engineering, and customer experience. Fleet-level systems determine how much of our capacity is sellable, how fast we can bring new clusters online, and how quickly a customer's workload recovers when hardware fails. You'll be defining the problem space as much as solving it, and you should be comfortable operating in ambiguity, with support from senior leadership as the initiative takes shape.
You'll collaborate closely with hardware partners, engineering, infrastructure operations, networking, supply chain, finance, and customer success to ensure cohesive lifecycle management across Crusoe Cloud. As a Staff PM, you'll lead significant cross-functional initiatives, influence technical and product strategy across multiple teams, and be accountable for outcomes that directly impact company goals.
What You'll Be Working On:
- Own the vision and product strategy for fleet management, including provisioning, burn-in, lifecycle management, health monitoring, maintenance orchestration, and performance optimization
- Drive outcomes across the entire product lifecycle, from discovery through launch and iteration, for fleet management capabilities
- Define product direction for systems managing large-scale GPU and CPU infrastructure, ensuring reliability, utilization, and operational efficiency
- Lead cross-functional initiatives spanning engineering, infrastructure operations, networking, and customer success to deliver integrated fleet management solutions
- Build consensus with tech leads and engineering managers on architecture decisions, tooling investments, and operational process improvements
- Identify opportunities to improve fleet utilization, reduce operational overhead, and enhance observability through product innovation
- Translate customer-facing reliability and performance impact, gathered through customer success, into product requirements
- Mentor other product managers and engineers, sharing expertise in infrastructure operations and complex systems thinking
What You'll Bring to the Team:
- 6+ years of product management experience delivering infrastructure or platform products, with demonstrated ownership of complex, cross-functional initiatives
- Deep expertise in fleet management concepts including provisioning, lifecycle management, monitoring, and operations at scale
- Strong understanding of distributed systems, server hardware, and infrastructure automation
- Proven ability to solve ambiguous, novel problems requiring research, invention, and creative analysis
- Experience driving product vision for complex systems with long-term strategic impact, balancing technical tradeoffs and operational requirements
- Track record of building consensus across engineering, operations, and infrastructure teams on technical direction and investment priorities
- Strong analytical skills interpreting operational metrics, performance data, and utilization patterns to guide product decisions
- Excellent communication skills articulating technical concepts and connecting them to business outcomes
- Experience mentoring colleagues in product management or technical roles
Bonus Points
- Experience with large-scale GPU infrastructure or AI/ML platform operations
- Familiarity with infrastructure automation tools, orchestration systems (Kubernetes, Slurm), or configuration management platforms
- Experience with observability platforms, monitoring systems, or incident management workflows
- Knowledge of hardware lifecycle management, predictive maintenance, or capacity planning systems
Benefits:
- Competitive compensation
- Restricted Stock Units
- Paid time off & paid holidays
- Comprehensive health, dental & vision insurance
- Employer contributions to HSA account
- Paid parental leave
- Paid life insurance, short-term and long-term disability
- Professional development & tuition reimbursement
- Mental health & wellness support
- Commuter benefits (parking & transit)
- Cell phone stipend
- 401(k) Retirement plan with company match up to 4% of salary
- Volunteer time off
Compensation Range
Compensation will be paid in the range of up to $215,000-$260,000 + Bonus. Restricted Stock Units are included in all offers. Compensation to be determined by the applicant's knowledge, education, 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.
Salary Context
This $215K-$260K range is above the 75th percentile for AI Product Manager roles in our dataset (median: $194K across 123 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 9,079 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At CRUSOE, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $220,200 based on 947 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($237K) sits 8% above the category median. Disclosed range: $215K to $260K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
CRUSOE AI Hiring
CRUSOE has 2 open AI roles right now. They're hiring across Data Scientist, AI Product Manager. Positions span Sunnyvale, CA, US, San Francisco, CA, US. Compensation range: $200K - $260K.
Location Context
AI roles in San Francisco pay a median of $261,000 across 2,646 tracked positions. That's 23% above the national median.
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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