← databricks / Staff Product Manager, AI Platform
tailored_resume_v2 / art_z6nhn7s0b5A
role
model
anthropic/claude-sonnet-4.6
created
2026-06-12T18:28
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What changed for databricks
| change | why it matters |
|---|---|
| Summary rewritten to lead with enterprise ML/AI platform scale (675M+ engagements, 50K TPS) and RL post-training workbench credential | JD's first requirement is 5+ years PM on platform/infrastructure products; Databricks team ships products used by thousands of sophisticated AI orgs — scale proof must lead |
| Intuit reordered to lead experience section and retitled to 'AI/ML Platform Infrastructure & Developer Frameworks' | Highest relevance score (5/5); 675M+ engagements and 50K TPS is the strongest enterprise platform proof point matching JD's scale expectations |
| RL Workbench moved to lead the Projects section | Directly maps to Databricks Mosaic AI model training platform; 12-algo benchmark with TRL/VeRL/OpenRLHF/NeMo RL is the single most differentiated credential for this role |
| Fintellect AI RAG/vector search bullet reframed to explicitly reference Databricks Vector Search | JD lists vector search as a core AI platform capability; ChromaDB + multi-provider LLM orchestration is genuine matching experience |
| Splunk retitled to 'Search & Data Infrastructure' and 10x query performance bullet elevated to lead | JD values deep infrastructure PM experience; 10x performance improvement on distributed query system is the strongest Splunk proof point for a data platform role |
| StreamIO and Fintellect condensed to 3 and 2 bullets respectively | Founder roles are supporting evidence for LLM/agent infrastructure hands-on experience; Intuit and Splunk carry the enterprise B2B weight — space allocated accordingly |
| BRAIN project bullet explicitly calls out MLflow experiment tracking and FastAPI model serving | MLflow is a named Databricks product in the JD; demonstrating hands-on MLflow usage strengthens platform credibility with the ML engineering team |
| IBM bullet reframed to emphasize 'former SWE foundation enabling credible engagement with world-class ML engineers' | JD states 'former software engineer experience is a significant plus' — the IBM role is the primary SWE credential and must be explicitly connected to that requirement |
| aeval project reframed around model monitoring and production ML systems governance | JD explicitly calls out model monitoring as a core AI Platform product area; aeval's CI/CD safety gates and statistical eval rigor map directly |
JD analysis (20 key phrases)
Key phrases: AI platformML lifecyclemodel trainingmodel servingfeature storesvector searchLLM infrastructuredistributed trainingreal-time inferenceexperimentation to productionenterprise adoptionML pipelinesoperationalize AI at scaledata and AI infrastructureproduction ML systemsMLflowUnity Catalogcommercialization strategyadoption bottlenecksplatform capabilities
Hard requirements:
- 5+ years PM experience on platform or infrastructure products
- Deep technical background (CS/EE or equivalent)
- Experience with ML/AI infrastructure (model training, serving, feature stores, vector search, LLM infrastructure)
- Proven enterprise B2B product management
- Shipped platform products with commercial outcomes
- Ability to write technical specs and engage with ML engineers
Preferred qualifications:
- Former software engineer experience
- Familiarity with recommendation systems
- Experience with distributed training architectures
- Real-time serving systems experience
- GTM/field enablement collaboration
Per-role mapping (10 roles scored)
| role | score | reframe angle | JD phrases that map |
|---|---|---|---|
| Streamio AI — Founder & CEO | 3/5 | LLM/agent infrastructure builder and 0-to-1 AI platform founder | LLM infrastructure, AI platform, experimentation to production, model serving |
| Fintellect AI — Founder & CEO | 3/5 | RAG/vector search pipeline builder; LLM orchestration at product scale | vector search, LLM infrastructure, ML pipelines, production ML systems |
| Intuit — Staff PM | 5/5 | Enterprise-scale AI/data platform PM with proven developer tooling and infrastructure delivery | platform capabilities, enterprise adoption, adoption bottlenecks, operationalize AI at scale, commercialization strategy, data and AI infrastructure |
| Splunk — Senior PM | 4/5 | Data infrastructure PM with distributed query systems and enterprise B2B delivery | data and AI infrastructure, ML pipelines, enterprise adoption, production ML systems, real-time inference |
| Kaiser Permanente — SOA Technical PM | 2/5 | Large-scale data infrastructure and platform-as-a-service delivery | data and AI infrastructure, platform capabilities |
| IBM — Software Engineer | 2/5 | Former software engineer with enterprise BI systems experience | deep technical background, former software engineer |
| Bank of America — MBA Associate | 1/5 | Quantitative analysis background | — |
| RL Workbench | 5/5 | Hands-on ML post-training infrastructure builder; directly maps to Databricks Mosaic AI | model training, LLM infrastructure, distributed training, ML lifecycle, experimentation to production |
| aeval — AI Model Evaluation Platform | 5/5 | ML evaluation and monitoring platform builder — maps to Databricks model monitoring | model serving, production ML systems, ML lifecycle, ML pipelines |
| BRAIN — Protein Structure Prediction | 4/5 | NeurIPS-published ML researcher with hands-on MLflow and model serving experience | model training, MLflow, model serving, ML lifecycle |
Tailored summary
Staff-level Technical PM with 12+ years shipping ML/AI infrastructure and developer platform products at enterprise scale — from scaling a data platform to 675M+ engagements and 50K TPS (Intuit) to building RL post-training workbenches that benchmark GRPO/DPO across TRL, VeRL, OpenRLHF, and NeMo RL today. Deep hands-on ML background: NeurIPS-published researcher, PyTorch/MLflow practitioner, and builder of production model training, evaluation, and RAG/vector search pipelines. Former software engineer (IBM, UC Berkeley) with a track record of translating enterprise ML team pain points into platform capabilities that accelerate the path from experimentation to production.