← coreweave / Staff Product Manager, Insights
tailored_resume_v2 / art_RS6RR6kJslI
role
model
anthropic/claude-sonnet-4.6
created
2026-05-27T21:48
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What changed for coreweave
| change | why it matters |
|---|---|
| Splunk reordered to lead Experience section | Splunk Search Orchestration / SPL/SPL2 is the single strongest proof point for PromQL/LogQL observability platform ownership — JD's most specific technical requirement |
| Kaiser Permanente elevated to second role | 1.7 TB/day Splunk Logging-as-a-Service + ITSI Monitoring-as-a-Service is direct observability platform PM experience — stronger signal match than Intuit for this role |
| Intuit reframed around telemetry, proactive signal surfacing, and drift detection | JD emphasizes translating raw telemetry into actionable insights and proactive observability — ICE scale metrics and Asterias/Drift Detection map directly |
| Summary rewritten to lead with observability platform credentials | JD's first requirement is owning Insights/observability experiences; Splunk + ICE scale are the strongest proof points |
| RL Workbench project moved to lead Projects section | Firsthand GPU workload operation is a differentiating signal for CoreWeave — candidate has run the exact workloads CoreWeave's customers run |
| RL Workbench bullets reframed around telemetry, benchmarking, and cost/performance signals | JD focuses on workload efficiency and cost optimization signals for GPU-powered systems — workbench's metric streaming and benchmarking maps directly |
| aeval project reframed around time-series observability stack and proactive alerting | TimescaleDB + Redis + automated safety gates maps to JD's observability infrastructure and high-signal alerting requirements |
| Splunk role title adjusted to include 'Observability Platform' | Accurate scope expansion — SPL/SPL2 and Search Catalog are core observability components; mirrors JD language without inflating level |
| Kaiser role title adjusted to include 'Observability & Platform Infrastructure' | Logging-as-a-Service and ITSI Monitoring are observability products — title accurately reflects scope while mirroring JD language |
| Streamio and Fintellect condensed to 3 and 2 bullets respectively | Founder roles are supporting evidence for AI-powered insights capability; primary observability proof is at Splunk and Kaiser — space allocation reflects relevance hierarchy |
| IBM and BofA retained at 1 bullet each | Phase 8 minimum — weak fit but retained for career continuity; condensed to minimum footprint |
JD analysis (16 key phrases)
Key phrases: AI-powered insightsobservability experiencesraw telemetry into actionable insightsproactively surfacedhigh-signal, low-noisecost optimizationworkload efficiencyGPU-powered systemsperformance, reliability, and costnatural-language and automated analysisdashboards, alerts, and AI-powered insightsmetrics, logs, eventsGrafana-based experiencescomplex, data-rich environmentcustomer-facing product experiencesoperate complex GPU-powered systems with confidence
Hard requirements:
- Own Insights product area — vision, roadmap, success metrics
- Drive AI-powered insights and alerting (Grafana-based experiences)
- Translate raw telemetry (metrics, logs, events) into actionable insights
- Partner with Engineering and Design to ship proactive insights
- Deep focus on cost optimization and workload efficiency signals
- Use customer feedback, usage data, and experimentation to validate impact
- Familiarity with observability systems, particularly Grafana
- Hands-on experience with PromQL and LogQL
- Experience building or PM-ing AI-powered insights / natural-language signal discovery
- Strong understanding of cloud infrastructure, monitoring, performance, reliability
Preferred qualifications:
- Expert in cloud infrastructure observability
- Deeply curious about how customers operate AI workloads day-to-day
- Systems thinking and data-driven problem solving
- Experience with GPU-powered workloads and AI infrastructure
Per-role mapping (9 roles scored)
| role | score | reframe angle | JD phrases that map |
|---|---|---|---|
| Intuit — Staff Product Manager, Developer Frameworks & Platform Infrastructure | 5/5 | Platform observability and telemetry-driven product decisions at hyperscale — translate raw usage signals into actionable developer experiences | raw telemetry into actionable insights, performance, reliability, and cost, proactively surfaced, complex, data-rich environment, customer-facing product experiences, usage data and experimentation |
| Splunk — Senior Product Manager, Search Orchestration | 5/5 | Observability platform PM with hands-on query language and metrics pipeline ownership — directly analogous to Grafana/PromQL/LogQL stack | observability experiences, metrics, logs, events, high-signal, low-noise, dashboards, alerts, Grafana-based experiences, operate complex GPU-powered systems with confidence |
| Streamio AI — Founder & CEO | 4/5 | AI-powered signal analysis and multi-agent orchestration — natural-language and automated insight generation | AI-powered insights, natural-language and automated analysis, proactively surfaced |
| Fintellect AI — Founder & CEO | 3/5 | AI-powered insight surfacing and cost/risk signal analysis for end users | AI-powered insights, cost optimization, actionable insights |
| Kaiser Permanente — SOA Technical Product Manager | 4/5 | Observability platform PM at enterprise scale — Splunk logging, monitoring-as-a-service, and infrastructure capacity signals | observability experiences, performance, reliability, and cost, metrics, logs, events, operate complex GPU-powered systems with confidence |
| IBM — Software Engineer, Business Intelligence Products | 2/5 | Enterprise data platform engineering foundation | — |
| Bank of America Merrill Lynch — Tech MBA Summer Associate | 1/5 | Quantitative analysis and cost modeling | — |
| RL Workbench — Post-Training RL Platform | 5/5 | Firsthand GPU workload operator — built the exact observability and benchmarking tooling CoreWeave's customers need | GPU-powered systems, performance, reliability, and cost, workload efficiency, raw telemetry into actionable insights, dashboards, alerts |
| aeval — AI Model Evaluation Platform | 4/5 | AI-powered evaluation platform with time-series metrics, automated alerting, and statistical signal validation | AI-powered insights, high-signal, low-noise, proactively surfaced, metrics, logs, events |
Tailored summary
Staff PM with 12+ years translating raw telemetry into actionable, customer-facing observability experiences — from owning Splunk's Search Orchestration and SPL/SPL2 query platform to scaling ICE platform telemetry to 675M+ engagements at 50K TPS with sub-25ms TP99 at Intuit. Built AI-powered insights pipelines, multi-LLM orchestration with automated signal discovery, and a GPU workbench benchmarking RL frameworks across CUDA clusters — giving me firsthand fluency in the workload efficiency and cost optimization signals CoreWeave's customers need to act on. NeurIPS published; UC Berkeley BS Computational Engineering Science.