← faire / Staff Product Manager, Search Algorithms
tailored_resume_v2 / art_peLB_LI-PRM
role
model
anthropic/claude-sonnet-4.6
created
2026-05-28T00:24
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What changed for faire
| change | why it matters |
|---|---|
| Splunk reordered to lead Experience section | Direct search system ownership (Search Service, Search Catalog, SPL) is the single strongest credential for a Search Algorithms PM role; JD's first hard requirement is 'complex Search systems' |
| Splunk bullets reframed to foreground search algorithms, failure mode diagnosis, and offline benchmarking language | JD explicitly asks for failure mode diagnosis and offline/online metrics refinement — Splunk's 10x query performance optimization maps directly |
| Intuit scaled to 675M+ engagements and 50K TPS moved to first bullet | JD requires 'high-scale products through state-of-the-art engineering'; enterprise scale proof belongs in first 2 bullets |
| Intuit bullets reframed around data-driven prioritization and velocity/scale/cost/modularity tradeoffs | JD key phrases 'velocity, scale, cost, and modularity' and 'offline and online metrics' map to Intuit's telemetry/BigQuery work |
| Streamio and Fintellect condensed to 3 and 2 bullets respectively | Founder credentials satisfy JD's 'startup founder' qualifier; detail condensed to preserve space for higher-relevance Splunk/Intuit roles |
| aeval moved to lead the Projects section | Offline metrics, statistical rigor (bootstrap CI, Welch's t-test), and regression detection directly mirror JD's requirement to 'refine suite of offline and online metrics' and 'diagnose failure modes' |
| Summary rewritten to lead with search system ownership at Splunk and scale at Intuit | JD's primary requirement is search system experience; summary must establish this credential in sentence 1 |
| Summary embeds 'high-scale products', 'state-of-the-art AI', 'startup founder', 'offline metrics', 'resourcefulness' language | These are exact JD key phrases; embedding them naturally signals fit without fabrication |
| Kaiser condensed to 2 bullets | Lower relevance to search algorithms role; retain for enterprise data platform scale signal but deprioritize |
| IBM and BofA retained at 1 bullet each | Hard rule: never cut a role entirely; both provide analytical depth signal at minimum |
JD analysis (18 key phrases)
Key phrases: search algorithmsdiscovery surfacesearch query segmentsoffline and online metricsfailure modeshigh-scale productsstate-of-the-art engineering and data scienceML/AI to solve business problemshigh-growth tech startupstartup foundercomplex Search systemsvelocity, scale, cost, and modularityrelentlessly resourcefulcreative problem-solvershop local movementretailersbrands and productsdata and machine learning
Hard requirements:
- 7+ years product experience at high-growth tech startup or as startup founder
- Experience working on complex Search systems
- Experience building high-scale products through state-of-the-art engineering and data science
- Regularly uses ML/AI to solve business problems
- Well-versed with recent advancements in ML/AI
Preferred qualifications:
- Experience with search query segmentation and failure mode diagnosis
- Experience defining offline and online metrics for search systems
- Ability to evaluate tradeoffs across velocity, scale, cost, and modularity
- Cross-functional leadership with engineering, data science, design
- Startup founder experience
Per-role mapping (10 roles scored)
| role | score | reframe angle | JD phrases that map |
|---|---|---|---|
| Splunk — Senior PM, Search Orchestration | 5/5 | Lead with search system ownership and query performance optimization as direct analog to Faire's search algorithms role | complex Search systems, search algorithms, failure modes, velocity, scale, cost, and modularity, offline and online metrics |
| Intuit — Staff PM, Developer Frameworks & Platform Infrastructure | 4/5 | Emphasize data-driven product decisions at massive scale and cross-functional platform leadership | high-scale products, state-of-the-art engineering and data science, ML/AI to solve business problems, velocity, scale, cost, and modularity |
| Streamio AI — Founder & CEO | 3/5 | Founder credibility + AI/ML product execution; condense to highlight startup founder experience and ML product delivery | startup founder, ML/AI to solve business problems, relentlessly resourceful, high-growth tech startup |
| Fintellect AI — Founder & CEO | 3/5 | Reinforce founder + ML/AI product credentials; condense significantly | startup founder, ML/AI to solve business problems, data and machine learning |
| Kaiser Permanente — SOA Technical PM | 2/5 | Condense to 1-2 bullets emphasizing data platform scale and technical PM depth | high-scale products, state-of-the-art engineering |
| IBM — Software Engineer | 1/5 | Single bullet; retain for career completeness | — |
| Bank of America Merrill Lynch — Tech MBA Associate | 1/5 | Single bullet; retain for analytical depth signal | — |
| RL Workbench | 3/5 | Frame as ML evaluation and benchmarking expertise relevant to search ranking model evaluation | offline and online metrics, ML/AI to solve business problems |
| aeval — AI Model Evaluation Platform | 4/5 | Lead projects section — directly maps to Faire's need to refine offline/online metrics and diagnose search system failure modes | offline and online metrics, failure modes, search algorithms |
| BRAIN — Protein Structure Prediction ML Platform | 3/5 | Reinforce ML depth and published research credentials | state-of-the-art engineering and data science, ML/AI to solve business problems |
Tailored summary
Technical Product Leader with 12+ years building high-scale products at the intersection of search, data, and ML — including direct ownership of Search Service, Search Catalog, and query performance optimization at Splunk, and scaling a platform to 675M+ engagements at Intuit. NeurIPS published ML researcher who regularly applies state-of-the-art AI to solve business problems, from RL post-training benchmarks to production evaluation platforms with rigorous offline metrics. Proven startup founder (2× companies) with the resourcefulness to move fast and execute 0-to-1 in ambiguous environments. BS Computational Engineering, UC Berkeley; MBA, Carnegie Mellon Tepper.