← coreweave / Staff Product Manager, Insights
tailored_resume_v2 / art_OHpWpUs7TcQ
role
model
anthropic/claude-sonnet-4.6
created
2026-05-27T21:44
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What changed for coreweave
| change | why it matters |
|---|---|
| Splunk reordered to lead Experience section | SPL/SPL2 query language ownership is the closest analog to PromQL/LogQL; Search Orchestration is the most direct observability PM credential for this role |
| Splunk title reframed to 'Search Orchestration & Observability' | Mirrors JD's observability language while remaining accurate to the scope of the role |
| Intuit bullets reordered to lead with 675M+ scale and telemetry/BigQuery bullets | JD emphasizes translating raw telemetry into insights at scale; these are the strongest proof points |
| Intuit Drift Detection bullet reframed to emphasize 'proactive, automated signal surfacing over manual discovery' | JD explicitly calls out proactively surfaced vs. manually discovered insights as a core product philosophy |
| Kaiser Permanente Splunk Logging-as-a-Service bullet moved to lead | 1.7TB daily observability platform ownership is directly relevant; surfaces the most credible observability credential from this role |
| RL Workbench reframed to lead projects section and emphasize GPU workload observability | CoreWeave's customers run exactly these AI training workloads; firsthand experience operating and monitoring them is a differentiator |
| aeval project reframed around proactive signal surfacing, statistical rigor, and observability stack | TimescaleDB + Redis + automated safety gates maps directly to the JD's observability and alerting requirements |
| Summary rewritten to lead with observability and telemetry credentials | JD's first requirement is owning observability experiences; Splunk + Intuit telemetry scale is the strongest opening credential |
| StreamIO and Fintellect bullets condensed and reframed around AI-powered insights and natural-language analysis | JD calls for AI-powered insights and natural-language signal discovery; these roles demonstrate that capability without overstating relevance |
| IBM and BofA each retained with single bullet | Anti-pattern compliance — never cut a role entirely; both contribute analytical and enterprise credibility |
JD analysis (18 key phrases)
Key phrases: observability experienceshigh-value signalsGPU-powered systemsactionable insightsAI-powered insightsproactively surfacedcost optimizationworkload efficiencyraw telemetrymetrics, logs, eventsGrafana-based experiencesnatural-language analysisperformance, reliability, and costdata-rich environmentAI workloadslow-noise informationusage data and experimentationcomplex GPU-powered systems
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 (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
- Entrepreneurial, independent thinking
Per-role mapping (9 roles scored)
| role | score | reframe angle | JD phrases that map |
|---|---|---|---|
| Intuit — Staff PM, Developer Frameworks & Platform Infrastructure | 5/5 | Platform observability and telemetry-driven product decisions at hyperscale — lead with data/telemetry and proactive signal surfacing | raw telemetry, actionable insights, proactively surfaced, usage data and experimentation, performance, reliability, and cost, high-value signals, data-rich environment |
| Splunk — Senior PM, Search Orchestration | 5/5 | Observability platform PM — SPL/SPL2 is directly analogous to PromQL/LogQL; search orchestration maps to telemetry pipeline ownership | observability experiences, metrics, logs, events, performance, reliability, and cost, workload efficiency, Grafana-based experiences, low-noise information |
| Kaiser Permanente — SOA Technical PM | 3/5 | Enterprise observability platform ownership — Logging-as-a-Service and monitoring-as-a-service directly relevant | observability experiences, performance, reliability, and cost, metrics, logs, events |
| StreamIO AI — Founder & CEO | 3/5 | AI-powered insights and automated signal discovery — natural-language analysis and agentic workflows | AI-powered insights, natural-language analysis, proactively surfaced |
| Fintellect AI — Founder & CEO | 3/5 | AI-powered insights translating complex data into actionable customer-facing experiences | AI-powered insights, actionable insights, cost optimization, natural-language analysis |
| IBM — Software Engineer, Business Intelligence | 2/5 | Enterprise data/BI foundation | — |
| Bank of America Merrill Lynch — Tech MBA Associate | 1/5 | Quantitative analytical foundation | — |
| RL Workbench — Post-Training RL Platform | 4/5 | Firsthand GPU workload observability — built the monitoring and benchmarking layer for AI training pipelines | GPU-powered systems, AI workloads, performance, reliability, and cost, workload efficiency, metrics, logs, events |
| aeval — AI Model Evaluation Platform | 4/5 | Built evaluation/observability platform with automated alerting and statistical signal validation | actionable insights, proactively surfaced, high-value signals, low-noise information, usage data and experimentation |
Tailored summary
Technical Product Leader with 12+ years building observability platforms, telemetry-driven developer experiences, and AI-powered insights at scale — from owning Splunk's Search Orchestration and SPL query language to scaling Intuit's ICE platform to 675M+ engagements with sub-25ms TP99 latency. Proven track record translating raw telemetry into actionable, proactively surfaced insights that drive customer action on performance, reliability, and cost. NeurIPS published researcher; firsthand experience operating GPU-powered AI workloads as a builder of RL post-training benchmarking infrastructure.