Who We Are

Vultr is on a mission to make high-performance cloud infrastructure easy to use, affordable, and locally accessible for enterprises and AI innovators around the world. With 33 global cloud data center locations, Vultr is trusted by hundreds of thousands of active customers across 185 countries for its flexible, scalable, global Cloud Compute, Cloud GPU, Bare Metal, and Cloud Storage solutions. In December 2024 Vultr announced an equity financing at a $3.5 billion valuation. Founded by David Aninowsky and self-funded for over a decade, Vultr has grown to become the world’s largest privately-held cloud infrastructure company.

Vultr Cares

100% company-paid insurance premiums for employee medical, dental and vision plans.

401(k) plan that matches 100% up to 4%, with immediate vesting

Professional Development Reimbursement of $2,500 each year

11 Holidays + Paid Time Off Accrual + Rollover Plan

Commitment matters to Vultr! Increased PTO at 3 year and 10 year anniversary + 1 month paid sabbatical every 5 years + Anniversary Bonus each year

$500 stipend for remote office setup in first year + $400 each following year

Internet reimbursement up to $75 per month

Gym membership reimbursement up to $50 per month

Company paid Wellable subscription

Join Vultr

Vultr is seeking a highly skilled and experienced Senior Product Manager, Observability to own the Observability solution that provides telemetry ingestion, querying, visualization, alerting, and retention for Vultr customers. The ideal candidate brings deep technical fluency in observability infrastructure, distributed systems monitoring, and cloud-native telemetry, combined with a strong product instinct for developer and operator experiences. This is a highly visible role in a high-growth technology company, which will require close partnership with Compute, Networking, and Platform teams to ensure every new infrastructure launch is observable by design. This is your opportunity to join our fast growing team and leave your mark on Vultr and the future of Cloud Infrastructure.

Key Responsibilities

Own the end-to-end Observability Platform roadmap across telemetry ingestion, querying, visualization, alerting, and retention for large-scale GPU clusters and multi-tenant cloud environments

Define Vultr's observability strategy across bare metal, VMs, Kubernetes, and managed services, aligned to infrastructure roadmap, reliability goals, and customer experience

Drive the customer-facing observability surface across dashboards, APIs, telemetry pipelines, and topology-aware insights

Translate low-level signals across GPU, CPU, memory, storage, and network into actionable health views, alerts, and debugging workflows for customers

Work closely with engineering on technical tradeoffs across metrics agents, collectors, data models, telemetry pipelines, APIs, and retention architecture

Build products for distributed AI environments by understanding how training and inference workloads behave across nodes, clusters, schedulers, and network fabrics

Define health models that help customers quickly identify degraded nodes, performance anomalies, and cluster bottlenecks at fleet scale

Ensure new infrastructure and platform launches are observable by design through strong partnership with compute, network, and platform teams

Stay current on modern observability stacks and AI infrastructure trends, including how GPU workloads change performance analysis, cost attribution, and operational workflows

Qualifications

7+ years of product management experience in cloud infrastructure, observability, monitoring, or developer platforms

Deep understanding of observability and monitoring systems, including metrics, logging, tracing, alerting, and telemetry pipeline architecture

Experience defining product strategy and roadmaps for platform or infrastructure products at scale

Strong technical background — ability to engage with engineering on telemetry agents, data models, query engines, retention, and distributed systems

Experience with GPU, AI/ML, or HPC infrastructure monitoring and the unique observability challenges of training and inference workloads